Last modified on Jul 23, 2026 13:45

Contributors: B. Fuentes Andrade (Institute of Environmental Physics (IUP)), M. Reuter (Institute of Environmental Physics (IUP)), M. Buchwitz (Institute of Environmental Physics (IUP))

Issued by: Institute of Environmental Physics (IUP), University of Bremen / Maximilian Reuter

Date: 09/02/2026

Ref: C3S2_313a_DLR_WP1-DDP-GHG-v2_PUGS_XGHG_v5.1

Official reference number service contract: 2024/C3S2_313a_DLR/SC1

History of modifications

List of datasets covered by this document

Acronyms

General definitions

Executive summary

This document is a Product User Guide and Specification (PUGS) generated in the framework of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). For C3S a large number of satellite-derived Essential Climate Variable (ECV) data products are generated and made available via the Copernicus Climate Data Store (CDS, https://cds.climate.copernicus.eu/). 

This document is the user guide for two satellite-derived atmospheric carbon dioxide (CO₂) and methane (CH₄) C3S data products, XCO2_OBS4MIPS and XCH4_OBS4MIPS, respectively. These two products, XCO2_OBS4MIPS and XCH4_OBS4MIPS, are collectively referred to as XGHG O4M for readability. CO₂ and CH₄ are important greenhouse gases (GHG). The two products are column-averaged dry-air mole fractions of CO₂ and CH_4, denoted as XCO₂ (in parts per million, ppm) and XCH₄ (in parts per billion, ppb), respectively. These two XGHG O4M products are generated within the C3S Greenhouse Gas Service (as part of the C3S Atmospheric Composition Service, currently led by DLR, Germany). In the following, this project is referred to as the C3S/GHG project. Within this project also mid-tropospheric CO₂ and CH₄ products are generated. These mid-tropospheric CO₂ and CH₄ products are not described in this document but in dedicated separate documents (ATBD MTGHG, 2024, PQAR MTGHG, 2024 and PUGS MTGHG, 2024).

The XGHG O4M products are merged multi-sensor XCO₂ and XCH₄ Level 3 (L3) products with monthly time and 1º x 1º spatial resolution, generated using an ensemble of individual satellite sensor Level 2 (L2) products. The current version of the products has been produced using a new merging algorithm, the Ensemble Median Algorithm (EMMA), in its version 5.1. EMMA v5.1 is based on the assumption that the ensemble median is a good choice for merging different input data products, analogous to previous EMMA versions, but it has a new design: it selects a reference L2 product per time period, while the ensemble of input L2 products are used for the computation of a spatio-temporal empirical adjustment for the reference product. This new design eliminates potential abrupt spatial jumps on monthly 10º x 10º grid cells from previous product versions. Other main improvements of the new merged product are the higher resolution of the merged L3 product, i.e. monthly 1º x 1º, compared to the previous monthly 5º x 5º resolution, and the inclusion of new input L2 products for both XCH₄ and XCO₂.

The two XGHG data products are generated - or have been generated in the past - from XCO₂ and XCH₄ products from the satellite instruments SCIAMACHY onboard ENVISAT (Burrows et al., 1995; Bovensmann et al., 1999), TANSO-FTS/GOSAT (Kuze et al., 2009, 2016), TANSO-FTS-2/GOSAT-2 (Suto et al., 2021). For product XCO2_OBS4MIPS also L2 products from NASA's OCO-2 and OCO-3 missions (Crisp et al., 2004; Boesch et al., 2011) have been used as input products. For XCH4_OBS4MIPS, a L2 product from TROPOMI/S5P (Veefkind et al., 2012).

The input data and the algorithm used to generate the product are described in  ATBD XGHG, 2026. The quality of the L3 OBS4MIPS products is described in PQAR XGHG, 2026. The algorithm has been developed at the University of Bremen, Germany, and is based on the algorithm used to generate previous versions of these products (e.g. Reuter et al., 2013, 2020).

1. Product Description(s)

In this section an overview of the version 5.1 XGHG O4M data products- specified in terms of variable, its property, processing levels and instruments - is given. 

Carbon dioxide and methane are so-called Essential Climate Variables (ECVs) and the need to monitor them has been clearly identified, including the definition of key requirements (TR-GAD GHG, 2024; ESA-CCI-GHG-URD, 2024; CMUG-RBD, 2012; GCOS-245; GCOS-200; GCOS-195; GCOS-154). In recent years several satellite-derived ECV data products, including CO₂ and CH₄ (e.g., Buchwitz et al., 2013a, 2016, 2017; Reuter et al., 2013, 2020; Schneising et al., 2023), have been generated in particular in the framework of the Climate Change Initiative (CCI) of ESA (European Space Agency) (e.g., Hollmann et al., 2013) and operationally via C3S.

Previous versions of these satellite-derived CO₂ and CH₄ data products have been used for several (primarily scientific) applications, e.g.,

• to improve our knowledge on the various natural and anthropogenic (surface) sources and sinks of these important greenhouse gases (GHG) (see, e.g., Alexe et al., 2015; Bergamaschi et al., 2015; Chevallier et al., 2014, 2016; Cressot et al., 2014; Detmers et al., 2015; Guerlet et al., 2013; Houweling et al., 2015; McNorton et al., 2016; Pandey et al., 2016; Reuter et al., 2014b, 2017; Schneising et al., 2014b, 2020; Turner et al., 2015, 2016, and references given therein),
• to monitor the global or regional distribution of CO₂ and CH₄ (e.g., Buchwitz et al., 2007, 2016b; Schneising et al., 2011; Frankenberg et al., 2011; Massart et al., 2016; Reuter et al., 2020; Hachmeister et al., 2022, 2024),
• to improve our knowledge on emission ratios, e.g., for biomass burning (e.g., Ross et al., 2013; Parker et al., 2016), and
• for comparisons with (chemistry) climate models (e.g., Shindell et al., 2013; Hayman et al., 2014; Lauer et al., 2017; Gier et al., 2020) and other models (e.g., Schneising et al., 2014a; Parker et al., 2016).

The data products presented here have been derived from satellite radiances in the Near Infrared / Short Wave Infrared (NIR/SWIR) spectral region using data from the following sensors: SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT, TANSO-FTS-2/GOSAT-2, OCO-2, OCO-3/ISS and TROPOMI/S5P. Satellite radiance observations in the NIR/SWIR spectral region in downward-looking observation modes are sensitive to atmospheric CO₂ and CH₄ concentration changes with good sensitivity down to the Earth's surface (because solar radiation reflected at the Earth's surface is observed). These measurements permit to retrieve total column information, but do not permit to retrieve (detailed) information on the vertical profiles of CO₂ and CH₄.

Figure 1 provides an overview of the satellites sensors used to generate the XGHG O4M products and whether they permit the retrieval of XCO₂, XCH₄ or both.

Figure 1: Satellite sensor time coverage and XGHG (i.e. XCO₂ and/or XCH₄) that each sensor permits.

The input Level 2 (L2) data products are satellite-derived column-averaged dry-air mole fractions of CO₂ and CH₄, denoted XCO₂ (in parts per million, ppm) and XCH₄ (in parts per billion, ppb).

The Level 3 (L3) data products generated from the merged individual L2 data products are in OBS4MIPS format and described in detail in Section 1.1 for XCO₂ and in Section 1.2 for XCH₄. OBS4MIPS (Observations for Model Intercomparisons Project https://pcmdi.github.io/obs4MIPs/ with last access: 2-Apr-2025) is an activity to make observational products more accessible especially for climate model intercomparisons.

The XCO₂ and XCH₄ OBS4MIPS products are gridded data products in NetCDF format with a spatial resolution of 1^ºx1^º (i.e., using an equirectangular (Cartesian) latitude/longitude grid) and a monthly time resolution. These products have been generated using as input the L2 EMMA (Ensemble Median Algorithm) v5.1 products (see ATBD XGHG, 2026). 

The EMMA algorithm takes the input L2 data products and merges them into a new L2 product. The core of EMMA v5.1 is based on the idea that the ensemble median is a good choice for merging different input data products, analogous to previous EMMA versions. However, the new approach, in contrast to former EMMA versions, is to select a reference L2 product per time period, while the ensemble of input L2 products are used for the computation of an empirical spatio-temporal adjustment of the reference product. The merged L2 product is again gridded to create the L3 OBS4MIPS product. The EMMA algorithm and its input data are described in detail in ATBD XGHG, 2026. The quality of the L3 OBS4MIPS products is described in PQAR XGHG, 2026.

The individual input L2 data products are bias corrected. Bias correction is usually an integral part of the L2 products and described in the corresponding literature. When merging the L2 products with EMMA, they are harmonized by adjusting them to a common a priori and by applying a global offset correction.

For the EMMA algorithm, also SLIM (Simple cLImatological Model for atmospheric CO₂ and CH₄ (Noël et al., 2022) and TCCON (Total Carbon Column Observing Network, Wunch et al., 2011) GGG2020 (Laughner et al., 2024) data are used. The SLIM data is used both as a new common a priori as well as to make a global offset correction. The TCCON data are essentially used to calibrate the reported uncertainties of the input L2 products and for the validation.

Table 1 lists the XCO₂ and XCH₄ data products presented in this document. These two products are merged multi-sensor XCO₂ and XCH₄ L3 products with monthly time and 1^ºx1^º spatial resolution generated using an ensemble of individual satellite sensor L2 products.

Table 1: 

We recommend users of these L3 products to read the ATBD XGHG, 2026, describing how this data product has been generated, and relevant peer-reviewed publications, i.e., Reuter et al., 2020, describing a previous version of this data product and how it can be used to address scientific applications.

The L2 data used as input to generate the XGHG O4M products is described in Section 2.2 of ATBD XGHG, 2026. Two additional datasets are used: in order to account for different column averaging kernels, all L2 retrieval results are adjusted to a common a priori, namely the SLIM. Scaling of the reported L2 uncertainties and validation are done with TCCON as reference data set. See ATBD XGHG, 2026 for a more detailed explanation.

1.1. OBS4MIPS XCO₂ description

The main quantity / data field of this product is the column-average dry-air mole fraction of atmospheric carbon dioxide (CO₂), denoted as XCO₂. XCO₂ in these products is a dimensionless quantity (unit: mol/mol) defined as the vertical column of CO₂ divided by the vertical column of dry air (= all air molecules except water vapor) (see, e.g., Buchwitz et al., 2005, for details).

The input data to generate the OBS4MIPS XCO₂ product is illustrated in Figure 6 in ATBD XGHG, 2026. For more detailed information on the input data and the processing algorithm see the ATBD XGHG, 2026 document.

In this section, version v5.1 of the XCO2_OBS4MIPS product is described. Table 2 shows a summary of the history of changes in previously generated XCO2_OBS4MIPS datasets.

1.2. OBS4MIPS XCH₄ description

The main quantity / data field of this product is the column-average dry-air mole fraction of atmospheric methane (CH₄). XCH₄ is a dimensionless quantity (unit: mol/mol) defined as the vertical column of CH₄ divided by the vertical column of dry air (= all air molecules except water vapor) (see, e.g., Buchwitz et al., 2005, for details).

The input data to generate the OBS4MIPS XCO₄ product is illustrated in Figure 7 in ATBD XGHG, 2026. For more detailed information on the input data and the processing algorithm see the ATBD XGHG, 2026 document.

In this section, version v5.1 of the XCH4_OBS4MIPS product is described. Table 3 shows a summary of the history of changes in previously generated XCH4_OBS4MIPS datasets.

1.3. Overview of Product Target Requirements

Target requirements are described in the Target Requirements and Gap Analysis document (TR-GAD GHG, 2024).

Table 4 compares the required and the achieved performance for random error (precision), required accuracy (in terms of spatio-temporal biases) and stability (in terms of linear bias drift). The data quality level is also summarized below (see Section 1.3 of PQAR XGHG, 2026 for a more detailed explanation on the reported probabilities). 

Table 4: 

1.4. Example visualization of key variables

Figure 2 and Figure 3 show example maps of XCO₂ and its corresponding uncertainty for the months of April 2014 and August 2015, respectively. The difference in the coverage for both months is due to the L2 algorithms used as a reference: in April 2014, GOSAT FOCAL was the reference algorithm, while in August 2015 the reference algorithm was OCO-2 FOCAL. 

Figure 2: XCO2_OBS4MIPS for April 2014 (panel a) and its uncertainty (panel b) computed from the retrieval noise and EMMA’s inter-algorithm spread. Gray areas indicate the absence of data.

Figure 3: 

Figure 4 and Figure 5 show example maps of XCH₄ and its corresponding uncertainty for the months of August 2015 and April 2019, respectively. As for XCO₂, the difference in the coverage for both months lies in the L2 algorithms used as a reference: in August 2015, GOSAT FOCAL-PR was the reference algorithm, while in April 2019 the reference algorithm was TROPOMI WFMD. 

Figure 4: 

Figure 5:  XCH4_OBS4MIPS for April 2019 (panel a) and its uncertainty (panel b) computed from the retrieval noise and EMMA’s inter-algorithm spread. Gray areas indicate the absence of data.

A XCO₂ value of, for example, 400 ppm at a given location means that 400 CO₂ molecules are present in the atmosphere above that location per one million air molecules excluding water molecules. XCO₂ can be retrieved from instruments such as SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT using Optimal Estimation (Rodgers, 2000) or DOAS (Buchwitz et al., 2000) retrieval algorithms as shown in various publications (e.g., Buchwitz et al., 2005; Butz et al., 2011; Cogan et al., 2012; Noël et al., 2021, 2022; Reuter et al., 2011; 2013; Schneising et al., 2011; Yoshida et al., 2013).

A XCH₄ value of, for example, 1800 ppb at a given location means that 1800 CH₄ molecules are present in the atmosphere above that location per one billion air molecules excluding water molecules. XCH₄ can be retrieved from instruments such as SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT using Optimal Estimation (Rodgers, 2000) or DOAS (Buchwitz et al., 2000) retrieval algorithms as shown in various publications (e.g., Buchwitz et al., 2005; Butz et al., 2011; Frankenberg et al., 2011; Schneising et al., 2011, 2023; Noël et al., 2022; Parker et al., 2011; Schepers et al., 2012; Yoshida et al., 2013).

Figure 6 shows XCO₂ time series for the northern (red) and southern (blue) hemisphere averages, as well as for the global average (black). The curves in the figure show some known features of atmospheric CO₂, such as a continuous increase in its yearly concentration, which results mainly from anthropogenic emissions due to the combustion of fossil fuels. Another feature that we can appreciate in the figure is the seasonality of atmospheric CO₂, with peaks in its concentration at the beginning of the vegetation growing season and a decrease in its concentration as the CO₂ uptake by plants starts to increase. The atmospheric CO₂ concentration increases again when the photosynthetic activity starts to decrease at the end of the summer. The oscillations in the CO₂ concentration are more pronounced in the northern hemisphere because of the higher amount of vegetation in this hemisphere compared to the southern hemisphere.

Figure 7 shows a similar plot for XCH₄. The curves shown in Figure 7 show as well a continuous increase of atmospheric CH₄. Its seasonal cycle is the result of the seasonality of its sources (like wetlands, rice paddies or biomass burning) and sinks (reaction with the hydroxyl radical), which are driven by meteorological conditions (e.g. Dowd et al., 2023).  A large uncertainty can be observed in the methane product for the time period until 2009, in which there is only data from SCIAMACHY. The reason for the large uncertainty in this period is a technical problem in important methane-sensitive detector pixels of the SCIAMACHY instrument.

The higher values of XCH₄ in the northern hemisphere compared to the southern hemisphere is clearly visible for XCH₄ (Figure 6), and less pronounced for XCO₂ (Figure 7). This results from higher emissions of both CO₂ and CH₄ in the northern hemisphere.

Figure 6: C3S XCO₂ time series (product XCO2_OBS4MIPS version 5.1) for the northern (red) and southern (blue) hemispheres, as well as global (black). Each dot stands for the XCO₂ monthly average over the zone indicated by the colors. Only data for latitudes between 60ºS and 60ºN and land fraction larger than 0.8 are used. The shaded areas show the corresponding root-mean-squared uncertainty consisting of (calibrated) measurement noise and inter-algorithm-spread, not to be confused with the uncertainty of the monthly XCO₂ average over the corresponding zone.

Figure 7: C3S XCH₄ time series (product XCH4_OBS4MIPS version 5.1) for the northern (red) and southern (blue) hemispheres, as well as global (black). Each dot stands for the XCH₄ monthly average over the zone indicated by the colors. Only data for latitudes between 60ºS and 60ºN and land fraction larger than 0.8 are shown. The shaded areas show the corresponding root-mean-squared uncertainty consisting of (calibrated) measurement noise and inter-algorithm-spread, not to be confused with the uncertainty of the monthly XCH₄ average over the corresponding zone.

1.5. Data usage information

The main quantity / data field is the column-average dry-air mole fraction of atmospheric of carbon dioxide (CO₂), denoted as XCO₂, for the XCO2_OBS4MIPS product. The column-average dry-air mole fraction of atmospheric methane (CH₄), denoted as XCH₄, is the main quantity / data field of the XCH4_OBS4MIPS product. We collectively refer to XCO₂ and XCH₄ as XGHG.

In contrast to  some other L2 products, in which the units used are ppm (10^-6) or ppb (10^-9), in these products, XCO₂ and XCH₄ are reported in units of mol/mol, defined as the vertical column of CO₂ or CH₄ divided by the vertical column of dry air (= all air molecules except water vapor) (see, e.g., Buchwitz et al., 2005, for details), reported as numerical fractional values in the range 0.0 – 1.0. For example, a XCO₂ value of 0.000400 corresponds to 400 ppm (parts per million); a XCH₄ value of 0.000001800 corresponds to 1800 ppb (parts per billion).

Also reported (in the same units) are the corresponding (1-sigma) uncertainties (variables xco2_stderr and xch4_stderr).

These variables are reported per month and per 1º x 1º (latitude times longitude) grid cell. 

Also provided are variables related to spatial (variables lat, lat_bnds, lon, lon_bnds) and temporal information (time, time_bnds) as described in detail in Section1.5.3. Information on altitude sensitivity (variable column_averaging_kernel) and a priori profiles (variables vmr_profile_co2_apriori and vmr_profile_ch4_apriori) is also provided.

1.5.1. Data format and file naming 

Each of the products (XCO2_OBS4MIPS and XCH4_OBS4MIPS) is contained in a single file using this file name convention: xghg_c3s_l3_v51_YYYYMM_YYYYMM.nc.

Explanation:

• xghg: Variable name (xco2 or xch4)
• c3s: Copernicus Climate Change Service
• l3: Level 3 product
• v51: Version 5.1
• YYYYMM_YYYYMM: First month and year and last month and year of data set.

For example, the product named xco2_c3s_l3_v51_200301_202412.nc refers to the L3 XCO2_OBS4MIPS product in its version 5.1, which covers the period from January 2003 to December 2024.

1.5.2. Quality flags and data masks 

The products contain no quality flags. Missing data is marked for each variable as described in Section 1.5.3.

Users have the option to use, for example, only data over land using variable land_fraction. Another option for masking is the boolean variable xghg_ensemble_availability, to use only data for grid cells in which the estimated empirical L2 spatio-temporal adjustment was computed from a significant amount of data from the ensemble (see ATBD XGHG, 2026 for more details).

1.5.3. File contents

Table 5 and Table 6 list the main characteristics of the XCO2_OBS4MIPS and XCH4_OBS4MIPS products, respectively. See ATBD XGHG, 2026, for an overview of how these products have been generated and for additional details.

Table 5: Main characteristics of the XCO₂ OBS4MIPs v5.1 product.

Table 6: Main characteristics of the XCH4 OBS4MIPs v5.1 product.

Each of the OBS4MIPS L3 products includes the variables listed in Table 7. They are described in the text following the table.

Table 7: OBS4MIPS variables. x, y, z, t, represent the number of grid points in longitude, latitude, pressure, and temporal dimension respectively.

Description of each parameter:

time: Time center in days since 1990-01-01. 

time_bnds: Time boundaries. Start and end time of each month in days since 1-Jan-1990 

lat: Latitude center in degrees north (from -90.0º to +90.0º). 

lat_bnds: Latitude boundaries (upper and lower boundaries of 1º latitude bands) in degrees north. 

lon: Longitude center in degrees east (from -180.0º to +180.0º).

lon_bnds: Longitude boundaries (upper and lower boundaries of 1º longitude bands) in degrees east.

pre: Pressure level center, dimensionless as normalized to surface pressure. 

pre_bnds: Pressure layer boundaries, dimensionless as normalized to surface pressure. 

land_fraction: Fraction of 1º x 1º cells covered by land (numerical values are between 0.0 and 1.0). It is computed from Global Land Cover Characterization (GLCC) data available from the U.S. Geological Survey (https://doi.org/10.5066/F7GB230D).

xghg = {xco2 or xch4}: Main parameter: satellite retrieved column-average dry-air mole fraction of CO₂ or CH₄, respectively. Typical values are << 1.0 (typically close to 0.0004 for XCO₂ and close to 0.0000018 for XCH₄) and 1.0E20 = no data. 

xghg_nobs = {xco2_nobs or xch4_nobs}: Number of individual XGHG L2 observations per grid box used to compute the reported L3 XGHG monthly average value (0 = no data).

xghg_ndays = {xco2_ndays or xch4_ndays}: Number of days with XGHG L2 observations per grid box, used as the effective number of independent observations to compute the SEOM of the L3 XGHG monthly averages included in the xghg_stderr variable (0 = no data).

xghg_stdder = {xco2_stderr or xch4_stderr}: Standard error of the average computed from the single sounding noise and potential seasonal and regional biases, i.e., the inter algorithm spread (1.0E20 = no data).

xghg_stddev = {xco2_stddev or xch4_stddev}:  Standard deviation of the L2 observations within each grid box (1.0E20 = no data).

xghg_ensemble_availability = {xco2_ensemble_availability or xch4_ensemble_availability}: Ensemble availability per grid box, expressed as a boolean. It informs of whether there were data in the ensemble to estimate a value of the empirical spatio-temporal adjustment (in which case its value is 0) or not (taking value 1). In the cases in which there is no ensemble to compute an empirical spatio-temporal adjustment for the reference product (i.e. for an ensemble availability of 1) the reference product is not (significantly) adjusted. (-9999 = no data)

column_averaging_kernel: XGHG averaging kernel (dimensionless); a vertical profile (1.0E20 = no data). The normalized column-averaging kernel represents the sensitivity of the retrieved XGHG to the true mole fraction depending on pressure (height). All values represent layer averages within the corresponding pressure levels. Values near one are ideal and indicate that the influence of the a priori is minimal. Profiles are ordered from the surface to the top of atmosphere. 

vmr_profile_ghg_apriori = {vmr_profile_co2_apriori or vmr_profile_ch4_apriori}: A priori dry-air mole fraction profile of atmospheric CO₂ or CH₄, respectively, expressed as a dimensionless fraction between 0.0 and 1.0 (1.0E20 = no data). All values represent layer averages within the corresponding pressure levels. Profiles are ordered from the surface to the top of atmosphere. The a priori profile is needed to apply the column averaging kernel.

1.5.4. Examples of known climate applications and best practices

The L3 OBS4MIPs XCO₂ and XCH₄ products have been primarily generated for comparison with climate models, see, for example Lauer et al., 2017, and Gier et al., 2020, but have also been used for other applications such as computations of annual mean atmospheric growth rates (e.g., Buchwitz et al., 2018; Reuter et al., 2020).

The L3 XCO₂ and XCH₄ v5.1 OBS4MIPS format data products described in this document (in combination with satellite XCO₂ and XCH₄ retrievals from the CAMS project https://atmosphere.copernicus.eu (last access: 5-Apr-2023), extending the OBS4MIPS data in time) have been used for the Copernicus Press Release issued in January 2023: "Copernicus: 2022 was a year of climate extremes, with record high temperatures and rising concentrations of greenhouse gases" https://climate.copernicus.eu/copernicus-2022-was-year-climate-extremes-record-high-temperatures-and-rising-concentrations (last access: 2-Mar-2026).

In the C3S Learning Hub, the interested readers can find a couple of Jupyter Notebooks (1, 2) that can help on the first steps, when exploring the data using python.

1.5.5. Known Issues and Limitations

Until 04/2009, the OBS4MIPs XCO₂ dataset was based solely on a SCIAMACHY dataset. This dataset is land-only and of lower quality than the later datasets, which has a direct impact on the OBS4MIPs product. As only one dataset was used during this period, the standard error could not be determined using the ensemble, but only from the validation study.

Until 04/2009, the OBS4MIPs XCH₄ dataset was based solely on a SCIAMACHY dataset. Due to technical issues affecting key methane-sensitive detector pixels of the SCIAMACHY instrument, the dataset is land-only from 11/2005 onwards and significantly inferior in quality compared to later datasets. This directly impacts the OBS4MIPs product. As only one dataset was used during this period, the standard error could not be determined using the ensemble, but only from the validation study.

In 01/2015, GOSAT had to switch to the secondary pointing mechanism due to degradation of the primary system. This affected all GOSAT L2 XCO₂ and XCH₄ L2 products used by EMMA. It resulted in a data gap in the OBS4MIPs XCH₄ product for this month, since a GOSAT product was used as a reference for the given month.

In 08/2017, OCO-2 was rebooted, resulting in a data gap in the OBS4MIPs XCO₂ product for this month, because an OCO-2 product was used as a reference for the given month.

The interested reader can find more information on the fitness for purpose of the XGHG datasets in the C3S Evaluation and Quality Control (EQC) Jupyter Book, where python code is provided to support the analyses (that could be potentially useful for the less experienced users).

2. Data access information

The data products and corresponding documentation are / will be made available via the Copernicus Climate Data Store (CDS): https://cds.climate.copernicus.eu/#!/home

Direct link to CO₂ products: https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview, DOI: 10.24381/cds.f74805c8

Direct link to CH₄ products: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview, DOI: 10.24381/cds.b25419f8

To download the data products, users have to select:

• Processing level: L3
• Variable: Column-average dry-air mole fraction
• Sensor and algorithm: MERGED and OBS4MIPS
• Version: 5.1

The OBS4MIPS XCO₂ and OBS4MIPS XCH₄ datasets are released for use under the CC-BY licence. Highlights and key features of the licence are provided here. Full legal text is provided here.

Users should first consult the user forum to find out if their question has already been answered. If not, users can use the ECMWF Support Portal to issue their request.

Technical questions, such as problems with data access, will be answered by ECMWF. Specific questions related to the data products will be answered by GHG retrieval experts. The aim is to respond as soon as possible (at least 85% if all questions will be answered within 15 working days).

Acknowledgements

References

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