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_PQAR_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 Quality Assessment Report (PQAR) 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 describes how the quality of two satellite-derived atmospheric carbon dioxide (CO₂) and methane (CH₄) data products has been assessed. 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 products are generated by the Institute of Environmental Physics (IUP), University of Bremen on behalf of the C3S. In the following this project is referred to as 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 two data products assessed in this document (XCO2_OBS4MIPS and XCH4_OBS4MIPS, collectively referred to as XGHG O4M for readability), are merged multi-sensor XCO₂ and XCH₄ Level 3 (L3) products with monthly time and 1ºx1º 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, i.e. EMMA v5.1. 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 (see ATBD XGHG, 2026 for details) . The merged L2 product is again gridded to create the L3 OBS4MIPS product.

The 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 absence of abrupt spatial jumps derived from the selection of different algorithms on different monthly 10º x 10º grid cells. Additionally, new input L2 products for both XCH₄ and XCO₂ have been included. See PUGS XGHG, 2026 for more details on the history of changes in previously generated datasets.

Specifically, this Product Quality Assessment Report (PQAR) at hand describes how version 5.1 of the XGHG O4M products has been validated using XCO₂ and XCH₄ retrievals from the Total Carbon Column Observing Network (TCCON, https://tccon.caltech.edu/) (Wunch et al., 2010, 2011, 2015, Laughner et al., 2024). Because the dataset has been produced using a new merging algorithm, this PQAR also contains a comparison between the current product version,  v5.1, and the last version produced with the former algorithm, v4.6, to ensure consistency. 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).

Summary product quality XCO2_OBS4MIPS v5.1:

The TCCON-validation of the XCO2_OBS4MIPS v5.1 product can be summarized as follows. There is only a small overall average bias of 0.29 ppm. Regional biases estimated from the site-to-site bias variability amount to 0.42 ppm. The average seasonal and spatiotemporal bias amounts to 0.24 ppm and 0.48 ppm, respectively. The overall linear drift is 0.00 ppm/year and its site-to-site variability 0.11 ppm/year. Therefore, it can be concluded that there is no significant drift in the data. The overall single-grid-cell precision is 1.82 ppm, and the uncertainty ratio, i.e. the ratio between the overall single-grid-cell precision and the average reported uncertainty is 1.10. 

The probability that the 0.5 ppm accuracy requirement is met is 70%. The probability that the 0.5 ppm/year stability requirement is met is 97%. Overall, this product has therefore reasonable accuracy and high stability. 

Summary product quality XCH4_OBS4MIPS v5.1:

The TCCON-validation of the XCH4_OBS4MIPS v5.1 product can be summarized as follows. There is only a small overall average bias of -6.39 ppb. Regional biases estimated from the site-to-site bias variability amount to 3.37 ppb. The average seasonal and spatiotemporal bias amounts to 2.68 ppb and 4.31 ppb, respectively. The overall linear drift of 0.11 ppb/year is much smaller than its site-to-site variability of 0.62 ppb/year. The overall single-grid-cell precision is 9.01 ppb and the uncertainty ratio, i.e. the ratio between the single-grid-cell precision and the average reported uncertainty and  is 0.82.

The probability that the 10 ppb accuracy requirement is met is 91%. The probability that the 3 ppb/year stability requirement is met is 99%. Overall, this product has therefore very good accuracy and high stability.

Product validation methodology

The validation method as used for the XGHG O4M version 5.1 products is based on comparisons of the XCO₂ and XCH₄ satellite-derived data products with corresponding XCO₂ and XCH₄ retrieval from the Total Carbon Column Observing Network (TCCON, http://www.tccon.caltech.edu/) (Wunch et al., 2010, 2011, 2015, Laughner et al., 2024).

Stochastic and (quasi-)systematic errors influence the usefulness of our data very differently. Stochastic errors average out quickly in global surface flux inversions, but large-scale (spatial and temporal) systematic errors, such as false inter-hemispheric gradients of a few tenths of a ppm, can significantly degrade the results. The opposite is true for smaller-scale applications. Due to the smaller scale, fewer data points are involved, making stochastic errors more important. However, an application using only data within Europe is not influenced by biased results in Australia. Therefore, we always aim at separating stochastic error components from systematic components. From the stochastic part we infer the precision or uncertainty. The systematic part is analysed in order to conclude on the accuracy. However, we split this term into four: the overall bias or offset, a regionally varying component, a seasonally varying component, and a linear drift. The first term is practically irrelevant, because, basically all applications only analyse gradients. However, the regionally or temporally varying parts of the systematic error are critical as mentioned above. As a measure for the variability of such biases, we compute the standard deviation of the regional and temporal parts of the systematic error. In order to address its regional or temporal character, we call the corresponding accuracy measure “relative systematic error” or “spatio-temporal bias”.

The current version of the products, v5.1, has been produced using a new merging algorithm. To ensure consistency between the new product v5.1 and the previous one, v4.6, a comparison between both datasets was carried out as explained in Section 1.5. 

Description of reference data used for validation

TCCON network

For validation of satellite XCO₂ and XCH₄ retrievals the Total Carbon Column Observing Network (TCCON, http://www.tccon.caltech.edu/) has been established (e.g., Wunch et al., 2010, 2011, 2015, Laughner et al., 2024). This network is the core network used for validation of the satellite XCO₂ and XCH₄ retrievals. Nevertheless, there are also some limitations as explained in Section 1.4.

TCCON provides XCO₂ and XCH₄ data products as retrieved from ground-based Fourier Transform Infrared (FTIR) observations based on direct sun observations. The TCCON data products can essentially be directly compared with the satellite-derived XCO₂ and XCH₄ data products, and have been used for this purpose extensively in the past as shown in many studies and publications (e.g., Reuter et al., 2020, and references given therein). 

The uncertainty of the TCCON reference data is:

• TCCON uncertainty XCO₂: 0.4 ppm (1-sigma)
• TCCON uncertainty XCH₄: 4 ppb (1-sigma),

see Wunch et al., 2010, but also the discussions of this uncertainty related to the use of TCCON data for the validation of satellite retrievals in Buchwitz et al. (2015, 2016), and Dils et al. (2014).

Traceability to WMO standard

As explained in this document, the satellite-derived XCO₂ and XCH₄ data products have been validated by comparison with TCCON XCO₂ and XCH₄ data products, which in turn have been calibrated against the World Meteorological Organization (WMO) in situ trace gas measurement scales (see Wunch et al., 2010), namely X2004 for XCH₄ and X2007 for XCO₂. This approach ensures that the satellite XCO₂ and XCH₄ retrievals are linked to the WMO standards for atmospheric CO₂ and CH₄ measurements.

Description of product validation methodology

In this section, we describe the method used to validate the gridded OBS4MIPS XCO₂ and XCH₄ L3 products. It is based on the validation method applied to precursor C3S L2 XCO₂ and XCH₄ data sets, but there are some important differences. The method has been adapted to validate L3 products instead of L2 products.

The OBS4MIPS XCO₂ and XCH₄ data products are validated with TCCON (Wunch et al., 2011) version GGG2020 (Laughner et al., 2024) measurements in a similar way as done by Reuter et al. 2011. For all comparisons, averaging kernels have been applied and the influence of the smoothing error reduced (Wunch et al., 2011). The TCCON data was gridded to the OBS4MIPS spatio-temporal grid. If multiple TCCON stations lie within the same grid cell, their corresponding gridded data is averaged, and so are their coordinates (longitude, latitude and altitude). To ensure that the TCCON measurements used for averaging are representative for the monthly mean, only grid cells with more than 100 TCCON measurements performed on 10 or more different days are taken into account. The criteria that define the co-locations between each of the XGHG O4M product and TCCON data are the following:

• having data for the same time grid,
• a maximum distance of 250 km between the (average) position of the TCCON station(s) and the centre of an 1ºx1º grid cell,
• a maximum difference of 250 m between the (average) altitude of the TCCON station(s) and the mean surface altitude of the area within the 1ºx1º grid cell.

The mean surface altitude at 1ºx1º resolution was computed by re-gridding the Copernicus GLO-90 Digital Elevation Model (DEM).

We only consider, for individual stations, time series that have more than 30 months of data so that the time series are long enough to ensure robust results.

For each data product several quality-related quantities have been determined as described in the following.

Per station statistics

For each TCCON station in the gridded TCCON data, the following performance statistics are calculated: number of co- locations, station bias (also known as regional bias), seasonal bias, linear drift, and single grid cell precision.

The main validation results are computed by fitting the following bias model to the difference between the satellite retrievals and the TCCON measurements at each TCCON site. 

\Delta X = a_0 + a_1 t + a_2 \sin (2 \pi t + a_3) + \epsilon. \qquad\qquad\qquad\qquad\text{(1)}

Here, 

\Delta X

represents the difference (satellite minus TCCON),

t

the time, in units of years, of the collocation between TCCON and the satellite measurements,

\epsilon

the residual, and 

a_{0-3}

the free fit parameters. Specifically,

a_0

represents an offset,

a_1

the linear drift and

a_2

the amplitude of the seasonal bias at a TCCON site.

The station or regional bias,

\Delta_{reg}

, is computed from the average over time of the fit model:

\Delta_{reg} = \overline{a_0 + a_1 t + a_2 \sin (2 \pi t + a_3)}, \qquad\qquad\qquad\qquad\text{(2)}

where the overline represents the mean.

The seasonal bias,

\Delta_{sea}

, is computed from the standard deviation (denoted as std) of the seasonal term:

\Delta_{sea} = std (a_2 \sin (2 \pi t + a_3) ). \qquad\qquad\qquad\qquad\text{(3)}

The spatiotemporal bias is computed from the regional and seasonal biases as

\Delta_{spt} = \sqrt{ \Delta_{reg}^2 + \Delta_{sea}^2} \qquad\qquad\qquad\qquad\text{(4)}

The linear drift, 

\Delta_{dri}

corresponds to the fit parameter 

a_1

, i.e. 

\Delta_{dri} = a_1.

The single grid cell precision,

\sigma

, is computed from the standard deviation of the residual:

\sigma = std(\epsilon) = std (\Delta X - a_0 - a_1 t - a_2 \sin (2 \pi t + a_3)) \qquad\qquad\qquad\qquad\text{(5)}

The OBS4MIPS products contain an uncertainty estimate for each grid cell (see ATBD XGHG, 2026). This uncertainty is meant to be the statistical uncertainty (1-sigma) associated with that grid cell value. We compute an average reported uncertainty,

\sigma_{rep}

, per station, as the quadratic average over time of the OBS4MIPS reported uncertainty,

\sigma'_{rep}

, for each grid cell.

\sigma_{rep} = \sqrt{\overline{\sigma'_{rep}^2}} \qquad\qquad\qquad\qquad\text{(6)}

Summarizing statistics

Based on the per station statistics, the following summarizing statistics are calculated.

The average station bias and the station-to-station bias variability (which we consider a measure for the regional biases), are computed as the mean and standard deviation of the individual station biases:

\overline{ \Delta_{reg}} \pm std(\Delta_{reg}).

The average seasonal bias is computed as the mean of the seasonal biases of all TCCON sites:

\overline{ \Delta_{sea} }.

The overall spatio-temporal bias,

\overline{ \Delta_{spt} }

, is computed as:

\overline{ \Delta_{spt} } = \sqrt{ std^2 ( \Delta_{reg} ) + \overline{ \Delta_{sea} }^2 }. \qquad\qquad\qquad\qquad\text{(7)}

The average linear drift and the drift uncertainty is computed by:

\overline{ \Delta_{dri} } \pm std (\Delta_{dri}).

As the linear drift can be assumed to be globally constant, the station-to-station standard deviation of the linear drift can be considered a measure of its uncertainty.

The overall single grid cell precision is computed by:

\overline{ \sigma } = \sqrt{ \overline{ \sigma^2}}, \qquad\qquad\qquad\qquad\text{(8)}

and the average reported uncertainty is given by:

\overline{ \sigma_{rep} } = \sqrt{ \overline{ \sigma_{rep}^2}}, \qquad\qquad\qquad\qquad\text{(9)}

To assess the quality of the OBS4MIPS uncertainty estimates, the overall single grid cell precision,

\overline{\sigma}

, is compared to the average reported uncertainty,

\overline{\sigma_{rep}}

. For that purpose, we computed the uncertainty ratio, defined as:

r = \frac{\overline{\sigma}}{\overline{\sigma_{rep}}}. \qquad\qquad\qquad\qquad\text{(10)}

If the reported uncertainty is realistic, it should approximately agree with the average precision estimated from the comparison with TCCON. In this case the uncertainty ratio r should be approximately unity. Therefore, the uncertainty ratio r  can be used to estimate if the reported uncertainty is realistic.

Methods for comparison of the achieved performance with the user requirements

The results obtained with the quantitative assessment methods described in Section 1.2.1 and Section 1.2.2 are compared with the Target Requirements (TRs) as given in the Target Requirement and Gap Analysis Document (TR-GAD) (TR-GAD GHG, 2024).

The user requirements are listed in the Target Requirement and Gap Analysis Document (TR-GAD GHG, 2024). They are based on requirements as formulated in documents GCOS-154, GCOS-195, GCOS-200, GCOS-245 and  CMUG-RBD, 2012.

To obtain a statement if a certain TR is met or not - or if it is "partially met" - several uncertainties are considered:

• The uncertainty of the estimated parameter (e.g., the uncertainties of the obtained values for "accuracy" and "stability").
• The uncertainty of the reference data (here: TCCON) (if not already included in the uncertainty of the obtained values for "accuracy" and "stability").
• The uncertainty of the comparison method (e.g., considering imperfect collocation of the satellite data and the reference data) (if not already included in the uncertainty of the obtained values for "accuracy" and "stability").

The following discussion is limited to "accuracy" and "stability" as these are the most critical / important data quality figures of merit (FoM) and because the TRs have been defined for them.

The TRs are the following (see also TR-GAD GHG, 2024):

• (Relative) Accuracy XCO₂: < 0.5 ppm (1-sigma)
• Stability XCO₂: < 0.5 ppm/year
• (Relative) Accuracy XCH₄: < 10 ppb (1-sigma)
• Stability XCH₄: < 3 ppb/year

(Relative) Accuracy

The term "accuracy" as used here means "relative accuracy" or "relative bias". For many applications (e.g., trend assessments or studies related to regional enhancements) a possible global offset is not critical but spatio-temporal biases are highly relevant. For example, for trend assessments the relative accuracy between different time periods is important (i.e., temporal biases would be critical) and for studies related to regional enhancements the spatial difference is important (i.e., spatial biases would be critical). Therefore, we focus in our performance assessments on “relative accuracy” rather than “absolute accuracy” (see also TRD-GAD GHG, 2024). Nevertheless, the global offset (a single number per product) has been determined and is reported in this document (and can be considered by the users if needed).

The relative accuracy is estimated from mean biases as obtained at the various TCCON validation sites as explained in detail in Section 1.2. The estimated achieved accuracy is in the following referred to as “ACC”. To compare this estimated achieved accuracy ACC with the TR requirement, one cannot simply compare the 2 numbers (by computing achieved minus required accuracy) but has to consider the uncertainty of the accuracy estimate. This uncertainty comes from the uncertainty of the reference data (here TCCON) and the uncertainty of the comparison method (e.g., colocation method and its representativity error). In the following the uncertainty of the accuracy estimate is referred to as “UNC_ACC” and we explain how it is computed and how it is used to estimate the probability that the accuracy target requirement is met.

The uncertainty of the TCCON reference data (see Wunch et al. (2010), but also the discussions of this uncertainty related to the use of TCCON data for the validation of satellite retrievals in Buchwitz et al., 2015, 2016, and Dils et al., 2014) is:

• TCCON uncertainty XCO₂: 0.4 ppm (1-sigma)
• TCCON uncertainty XCH₄: 4 ppb (1-sigma)

These uncertainties are increased by 50% to also consider other error sources, especially error of the comparison method such as the representativity error. The assumed uncertainty ("UNC_ACC") of the estimated accuracy value ("ACC") are therefore:

• UNC_ACC XCO₂: 0.6 ppm (1-sigma)
• UNC_ACC XCH₄: 6 ppb (1-sigma)

In summary, we now have ACC +/- UNC_ACC (1-sigma) for the estimated relative accuracy or spatio-temporal bias and its uncertainty. These values are interpreted as the mean and the standard deviation of an underlying probability density function (pdf).

ACC is a non-negative number and the Target Requirement (TR) for accuracy defines an “acceptable range” or interval of “acceptable” accuracy values as [0, TR[, i.e., in order to meet the requirements ACC shall be smaller than TR but will be larger than (or equal to) zero. Because of this non-negativity, ACC cannot be distributed according to a Gaussian (“normal”) probability density function (pdf) (esp. if the mean is much smaller than the standard deviation) but it may be reasonable to assume that the overall distribution is a lognormal distribution (last accessed 15/11/2023), with parameters selected such that the lognormal pdf is very similar to a Gaussian pdf if the mean is on the order or larger than the standard deviation. 

The probability density function (pdf) of the lognormal distribution is:

y = f(x|\mu, \sigma) = \frac{1}{x \sigma \sqrt{2 \pi}} \exp\left(- \frac{(\log x - \mu)^2}{2 \sigma^2}\right), \quad \text{for} \ x \ > \ 0. \qquad\qquad\qquad\qquad\text{(11)}

The lognormal distribution has parameters µ and σ, which are related to parameters mean m = ACC and variance ν = UNC_ACC² as follows:

\mu = \log \left( \frac{m^2}{\sqrt{\nu + m^2}} \right), \qquad\qquad\qquad\qquad\text{(12)}

\sigma = \sqrt{\log \left( \frac{\nu}{m^2} + 1 \right)}. \qquad\qquad\qquad\qquad\text{(13)}

The cumulative distribution function (cdf) of the lognormal distribution is:

P = F(x|\mu, \sigma) = \frac{1}{\sigma \sqrt{2 \pi}} \int_0^x \frac {1}{t} \exp\left(- \frac{(\log t - \mu)^2}{2 \sigma^2}\right) \text{d}t, \quad \text{for} \ x \ > \ 0. \qquad\qquad\qquad\qquad\text{(14)}

This function is used to compute the probability, that the accuracy requirement is met, see Figure 1 for XCO₂ and Figure 2 for XCH₄.

Figure 1: Probability that the XCO₂ accuracy TR is met as a function of the achieved accuracy. 

Figure 2: Probability that the XCH₄ accuracy TR is met as a function of the achieved accuracy.

Stability

For the TR assessment, the stability assessment is limited to the linear bias trend / drift.

As for the accuracy assessment, it is assumed that the value for stability has been obtained assuming error free TCCON observations and an error free comparison method. In contrast to the accuracy assessment approach, it is assumed that the uncertainty of the stability value is known (it corresponds to the (1-sigma) slope (SLO) error of the linear fit). The result of the stability assessment is: STA +/- UNC_SLO. 

To consider the uncertainty of the reference data we assume that the TCCON data approximately meet the following stability requirements:

• XCO₂ stability: 0.2 ppm/year
• XCH₄ stability: 1 ppb/year

These uncertainties need to be added quadratically (via Root-Sum-Square (RSS)) to UNC_SLO to obtain the overall uncertainty UNC_STA. 

As shown in Table S-1 for XCO₂ and Table S-2 for XCH₄ in column "Long-term drift" in document PVIRv5 (Buchwitz et al., 2017) typical values for STA +/- UNC_SLO are (if the uncertainty is converted to 1-sigma):

• XCO₂: +0.1 +/- 0.07 (1-sigma) ppm/year
• XCH₄: -0.8 +/- 0.4 (1-sigma) ppb/year

These values are listed here only for illustration (the exact value depends on product and assessment method). 

Quadratically adding the assumed TCCON uncertainty gives for this example for STA +/- UNC_STA:

• XCO₂: +0.1 +/- 0.21 (1-sigma) ppm/year
• XCH₄: -0.8 +/- 1.08 (1-sigma) ppb/year

In contrast to ACC, STA can also be negative and we use a Gaussian probability density function N(x, mean=STA, sigma=UNC_STA) to compute the probability that the stability TR is met. This probability is the integral of N over the interval as defined by the stability TR requirement, i.e., interval [-TR, +TR], or simply the difference between two different values of the cumulative distribution function Nc(x, mean=STA, sigma=UNC_STA) (namely at x=TR and x=-TR). The probability P that the stability TR is met for XCO₂ for a given value of STA is therefore for this example: P(STA) = Nc (+0.5, mean=+0.1, sigma=0.2) – Nc(-0.5, mean=+0.1, sigma=0.2) = 97%. This means that in these cases it is almost certain that the stability TR is met.

Figure 3 shows typical probability functions.

Figure 3: Probability functions used to obtain the probability that the stability requirement is met. Top: for XCO2. Bottom: for XCH4.

Known limitations

The TCCON network (see also the TCCON wiki pages for the TCCON Sites and for the Sites Pages) consists of the sites shown in Figure 4. It is relatively dense in the USA, in Europe and in Japan, but overall the TCCON network is relatively sparse (e.g., no or only very few sites in Russia, South America and Africa) and does not cover all areas with conditions that (can) affect the quality of the satellite XCO₂ and XCH₄ retrievals (e.g., deserts due to their high surface albedo combined with potentially high amounts of specific aerosol types such as desert dust storm mineral aerosols). Typically, the TCCON network underrepresents desert-like surfaces, open oceans, the tropics and the southern hemisphere in general.

It should also be noted that some bias correction methods of the individual L2 products contributing to EMMA use TCCON data, which can reduce the independence between the satellite product and the validation data set to some degree.

The TCCON project started in 2004 and the number of operational sites has increased over time. The TCCON Network provides temporally dense observations, but delays in data availability can limit their timeliness for validation purposes.

Figure 4: Location of TCCON sites. Source: https://tccondata.org/.

The TCCON network is the core reference network for the validation of the satellite XCO₂ and XCH₄ retrievals and is therefore essential for this part of the C3S service.

However, several limitations affect its current use for validation:

• The spatial coverage of the network is limited and does not fully represent all geophysical conditions relevant for satellite retrieval quality assessment.
• Data availability can be delayed, which impacts the timeliness of validation activities.
• The long-term sustainability of parts of the network is uncertain, as continued funding is not guaranteed.

Consistency with previously delivered OBS4MIPS version v4.6

To compare the XGHG O4M v5.1 and v4.6 data products, they were converted to the same resolution. For that, the XGHG variable of v5.1 was regridded to a 5ºx5º spatial grid using a weighted average, where the weights were the number of L2 observations within each 1ºx1º grid box normalized to the total number of L2 observations within the corresponding 5ºx5º grid box.

For a qualitative comparison, some examples of global maps for the original v5.1 products, the re-gridded v5.1 products and the v4.6 products are shown. Additionally, the re-gridded v5.1 products and the v4.6 products are brought to the same sampling to plot the time series for different latitude bands.

For a more quantitative comparison, the re-gridded v5.1 products are plotted against the v4.6 products and fitted to a straight line.

The results of these comparisons are shown in Section 2.3 for XCO₂ and Section 2.4 for XCH₄.

Validation results

In this section, detailed validation results are shown. The validation results for the Level 3 XCO₂ product are presented in Section 2.1 and the XCH₄ validation results are shown in Section 2.2. For each data product a set of well defined FoMs is computed to summarize the validation results. 

The validation is based on comparisons with TCCON XCO₂ and XCH₄ retrievals. For the results presented in this document, the TCCON station IDs as shown in Table 1 have been used to name each corresponding TCCON station. 

Table 1: Station IDs corresponding to each TCCON Station name.

Validation results for Level 3 OBS4MIPS XCO₂ product

Figure 5 shows all 11338 co-located OBS4MIPS and TCCON XCO₂ gridded results used for the validation study. One can see that the temporal sampling differs from site to site and that the L3 OBS4MIPS XCO₂ product captures the year-to-year increase and the seasonal features well.

The overall agreement of the OBS4MIPS data with TCCON data at all sites is illustrated in Figure 6 and Figure 7.  Figure 6 shows the histogram of the difference OBS4MIPS – TCCON, displaying a near Gaussian distribution with a center at 0.22 ppm and a standard deviation of 1.26 ppm. The OBS4MIPS vs. TCCON plot in Figure 7 shows a pronounced clustering along the one-to-one line. This is supported by a good agreement of the orthogonal distance regression with the one to-one line and high Pearson correlation coefficient of 0.9788. These results provide a first rough overview of the agreement between the OBS4MIPS XCO₂ dataset with TCCON.

The results of all site performance statistics as well as the summarizing performance statistics are illustrated in Figure 8 and listed in Table 2. The Product Quality Summary Table for the OBS4MIPS XCO₂ product is shown as Table 3. 

Figure 5: Co-located OBS4MIPS and TCCON XCO₂ results used for the validation study.

Figure 6: Normalized histogram of the difference XCO₂ OBS4MIPs minus TCCON.

Figure 7: XCO₂ OBS4MIPS vs. TCCON data, including one-to-one line (black), orthogonal distance regression (ODR, red), and Pearson correlation coefficient.

Figure 8: Validation results for XCO₂ OBS4MIPS monthly averages. From left to right, the figure shows the per site performance statistics explained in Section 1.2.1, i.e. regional

(\Delta_{reg}),

seasonal

(\Delta_{sea}),

and spatiotemporal biases

(\Delta_{spt}),

the linear drift

(\Delta_{dri}),

the single cell

(\sigma)

and reported precision

(\sigma_{rep})

and the number of soundings ( # ). TCCON sites are ordered from top to bottom by average latitude of the co-located satellite soundings. The last row includes the summarizing performance statistics as defined in Section 1.2.2.

Table 2: Validation results for XCO₂ OBS4MIPS. From left to right, the table shows the per site performance statistics explained in Section 1.2.1, i.e. regional

(\Delta_{reg}),

seasonal

(\Delta_{sea}),

and spatiotemporal biases

(\Delta_{spt}),

the linear drift

(\Delta_{dri}),

the single cell

(\sigma)

and reported precision

(\sigma_{rep})

and the number of soundings ( # ). TCCON sites are order from top to bottom by average latitude of the co-located satellite soundings. The last row includes the summarizing performance statistics as defined in Section 1.2.2.

The validation results show that there is only a small overall average station bias of 0.29 ppm. Regional biases estimated from the site-to-site bias variability amount to 0.42 ppm. The average seasonal and spatiotemporal bias amounts to 0.24 ppm and 0.48 ppm, respectively. The overall linear drift of 0.00 ppm/year is much smaller than its site-to-site variability of 0.11 ppm/year and, therefore, considered not significant. The uncertainty ratio, i.e. the ratio between the single grid cell precision, 

\sigma,

and the reported uncertainty,

\sigma_{rep},

is 1.10,  which is close to unity, indicating good agreement between the OBS4MIPS reported uncertainty and the overall precision.

Table 3: Product Quality Summary Table for product XCO2_OBS4MIPS.

The probability that the 0.5 ppm accuracy requirement is met is 70%.

The probability that the 0.5 ppm/year stability requirement is met is 97%.

Overall, this product has therefore reasonable accuracy and high stability. 

Validation results for Level 3 OBS4MIPS XCH₄ product

Figure 9 shows all 19246 co-located OBS4MIPS and TCCON XCH₄ gridded results used for the validation study. One can see that the temporal sampling differs from site to site and that the L3 OBS4MIPS XCH₄ product captures the year-to-year increase and the seasonal features well.

The overall agreement of the OBS4MIPS data with TCCON data at all sites is illustrated in Figure 10 and Figure 11. The histogram of the difference OBS4MIPS – TCCON (Figure 10) shows a near Gaussian distribution with a center at -7.07 ppb and a standard deviation of 8.08 ppb. The OBS4MIPS vs. TCCON plot (Figure 11) shows a pronounced clustering along the one-to-one line. This is supported by a good agreement of the orthogonal distance regression with the one to-one line and high Pearson correlation coefficient of 0.9702. These results provide a first rough overview of the agreement between the OBS4MIPS XCH₄ dataset with TCCON.

The results of all site performance statistics as well as the summarizing performance statistics are illustrated in Figure 12 and listed in Table 4.

The Product Quality Summary Table for the OBS4MIPS XCH₄ product is shown as Table 5. 

Figure 9: Co-located OBS4MIPS and TCCON XCH₄ results used for the validation study.

Figure 10: Normalized histogram of the difference XCH₄ OBS4MIPs minus TCCON.

Figure 11: XCH₄ OBS4MIPS vs. TCCON data, including one-to-one line (black), orthogonal distance regression (ODR, red), and Pearson correlation coefficient.

 Figure 12: Validation results for OBS4MIPS monthly averages. From left to right, the figure shows the per site performance statistics explained in Section 1.2.1, i.e. regional

(\Delta_{reg}),

seasonal

(\Delta_{sea}),

and spatiotemporal biases

(\Delta_{spt}),

the linear drift

(\Delta_{dri}),

the single cell

(\sigma)

and reported precision

(\sigma_{rep})

and the number of soundings ( # ). TCCON sites are ordered from top to bottom by average latitude of the co-located satellite soundings. The last row includes the summarizing performance statistics as defined in Section 1.2.2.

Table 4: Validation results for XCH₄ OBS4MIPS. From left to right, the table shows the per site performance statistics explained in Section 1.2.1, i.e. regional

(\Delta_{reg}),

seasonal

(\Delta_{sea}),

and spatiotemporal biases

(\Delta_{spt}),

the linear drift

(\Delta_{dri}),

the single cell

(\sigma)

and reported precision

(\sigma_{rep})

and the number of soundings ( # ). TCCON sites are order from top to bottom by average latitude of the co-located satellite soundings. The last row includes the summarizing performance statistics as defined in Section 1.2.2.

The validation results show that there is only a small overall average station bias of -6.29 ppb. Regional biases estimated from the site-to-site bias variability amount to 3.37 ppb. The average seasonal and spatio-temporal bias amounts to 2.68 ppb and 4.31 ppb, respectively. The overall linear drift of 0.11 ppb/year is much smaller than its site-to-site variability of 0.62 ppb/year and, therefore, considered not significant.

The uncertainty ratio, the ratio between the single grid cell precision,

\sigma,

and the reported uncertainty,

\sigma_{rep},

is 0.82, which is close to unity, indicating good agreement between the OBS4MIPS reported uncertainty and the overall precision.

Table 5: Product Quality Summary Table for product XCH4_OBS4MIPS.

The probability that the 10 ppb accuracy requirement is met is 91%.

The probability that the 3 ppb/year stability requirement is met is 99%. 

Overall, this product has therefore very good accuracy and high stability.

Comparison results for Level 3 OBS4MIPS XCO₂ v.5.1 and v4.6 products

Examples of global maps for the v5.1 product at its original resolution, the re-gridded v5.1 product to match the v4.6 resolution and the v4.6 product are shown in Figure 13 (for May 2020) and Figure 14 (for July 2020). In the figures, we can see that both the v5.1 and v4.6 datasets present very similar features. For example, Figure 13 shows global maps for May 2020, corresponding to the beginning of the vegetation growing season in the northern hemisphere, i.e. when the atmospheric CO₂ concentration typically reaches its maximum values, leading to a pronounced north-south gradient in XCO₂. Figure 14 shows global maps for July 2020, after the photosynthetic CO₂ uptake by the plants grown in the northern hemisphere, leading to a decrease in the atmospheric CO₂ concentration and an inversion of the north-south gradient. Regarding the spatial coverage, we can appreciate in both Figure 13 and Figure 14 that the (re-gridded) v5.1 product has a more complete coverage.

Figure 13: Example of a Obs4MIPs XCO₂ global map for May 2020 showing (a) v5.1 in its original 1°x1° spatial resolution, (b) v5.1 re-gridded to match the v4.6 5°x5° resolution, and (c) v4.6 in its original 5°x5° spatial resolution.

Figure 14: Same as Figure 13, but showing an Obs4MIPs XCO₂ global map for July 2020.

The plots of the re-gridded v5.1 products against the v4.6 products are shown in Figure 15, where we can see that most data lie near the one-to-one line. The fit to a straight line yields both a slope and coefficient of determination very close to one, showing good agreement between both datasets. For the lowest XCO₂ values (below approximately 380 ppm) both datasets agree almost perfectly. These values correspond to the period in which SCIAMACHY BESD is the reference and the only available product, so both datasets are almost identical in this period because there is no ensemble from which to compute the empirical spatio-temporal adjustment.

Figure 15: Scatter plot of Obs4MIPs v4.6 XCO₂ against Obs4MIPs v5.1 XCO₂ (re-gridded to the v4.6 resolution). Each point represents a grid box (5°x5°) containing data in both datasets. The data was fitted to a straight line (displayed as a red line), yielding both a slope and a coefficient of determination (r²) very close to one (see the shaded box). Marker colours indicate the number of counts (note the logarithmic scale of the colour bar) in a 2-D histogram, showing that the data concentrates near the one-to-one line (black).

Finally, Figure 16 shows the time series for three latitude bands for both v5.1 and v4.6. The plot shows, for each month and latitude band, the mean XCO₂ values. Only grid boxes with XCO₂ data from both v4.6 and the re-gridded v5.1 products were used for the plot. Both datasets exhibit very similar behaviour in the time series.

Figure 16: Time series for XCO₂ Obs4MIPs v5.1 (blue) and superposed XCO₂ Obs4MIPs v4.6 (orange). The dashed gray vertical lines mark the changes in the reference product, i.e. from the beginning of the dataset until the end of March 2009, SCIAMACHY BESD was used as the reference; from April 2009 until the end of August 2014, GOSAT FOCAL was the reference; and from September 2014 until the end of the dataset period, the reference product was OCO-2 FOCAL. Three latitude bands were used, shown in the corresponding panels: panel (a) shows the latitude band from 20º N - 60º N, panel (b) shows 20º S to 20º N, and panel (c) shows 60º S to 20º S.

The random error, accuracy and stability estimates obtained for the v5.1 and v4.6 XCO₂ datasets are shown in Table 6. Overall, the direction of change in the mean bias and stability suggests improvement whilst there is regression in the accuracy. However, direct comparison of the parameters obtained for the different versions have nuances. First of all, the time periods covering the two versions of the datasets is different, as the v5.1 datasets covers one year more than the v4.6 datasets. Additionally, the definition of collocations used for the validation of both versions of the datasets is slightly different, which together with the different spatial coverage of the datasets for the different versions, leads to the use of different sets of collocations, and even different TCCON stations, for the validation. As a result, and taking into account also the TCCON uncertainties, we consider the small changes between versions in the obtained mean bias, accuracy and stability parameters as non-significant, showing consistency between versions. Finally, a direct comparison of the random error estimates does not reflect a change in the performance of the dataset due to the different spatial resolution of the v5.1 dataset (1º x 1º) and the v4.6 dataset (5º x 5º), since higher random errors are expected for higher spatial resolution.

Table 6: Comparison of random error, accuracy and stability estimates obtained from v5.1 and v4.6 XCO₂ datasets, together with an assessment of their corresponding change.

Comparison results for Level 3 OBS4MIPS XCH₄ v.5.1 and v4.6 products

Analogously to Section 2.3., examples of global maps for the XCH₄ v5.1 product in its original resolution, the re-gridded v5.1 product and the v4.6 product are shown in Figure 17 (for April 2020) and Figure 18 (for October 2020). As for XCO₂, both the v5.1 and v4.6 datasets represent similar features, such as the spatial latitudinal gradients or regionally enhanced values. Additionally, we can appreciate in both Figure 17 and Figure 18 a significant improvement in the spatial coverage of the re-gridded v5.1 dataset with respect to v4.6.

Figure 17: Same as Figure 13, but showing an Obs4MIPs XCH₄ global map for April 2020.

Figure 18: Same as Figure 13, but showing an Obs4MIPs XCH₄ global map for October 2020.

The plots of the re-gridded v5.1 products against the v4.6 products are shown in Figure 19, where we can see, as for XCO₂, that most data lie near the one-to-one line. The fit to a straight line yields both a slope and correlation coefficient very close to one, showing good agreement between both datasets. For the lowest XCH₄ values (below approximately 1630 ppb) both datasets agree almost perfectly. These values correspond to the period in which SCIAMACHY WFMD is the reference and the only available product, so both datasets are almost identical in this period because there is no ensemble from which to compute the empirical spatio-temporal adjustment.

Figure 19: Same as Figure 13, but for Obs4MIPs v4.6 XCH₄ against Obs4MIPs v5.1 XCH₄ (re-gridded to the v4.6 resolution).

Figure 20 shows the time series for three latitude bands for both v5.1 and v4.6. Analogous to Figure 16, the plot shows, for each month and latitude band, the mean XCH₄ values. Only grid boxes with XCH₄ data from both v4.6 and the re-gridded v5.1 products were used for the plot. Both datasets exhibit very similar behaviours in the time series, showing only a small offset in the three latitude bands until April 2009 (i.e. the time in which SCIAMACHY BESD is the reference and only avalilable product), and a very small discrepancy in the latitude band 20°S-20°N towards the end of the data series.

Figure 20: Same as Figure 14, but for time series for XCH₄ Obs4MIPs v5.1 (blue) and superposed XCH₄ Obs4MIPs v4.6 (orange). The dashed gray vertical lines mark the changes in the reference product, i.e. from the beginning of the dataset until the end of March 2009, SCIAMACHY WFMD was used as the reference; from April 2009 until the end of April 2018, GOSAT FOCAL PR was the reference; and from May 2018 until the end of the dataset period, the reference product was TROPOMI WFMD.

The random error, accuracy and stability parameters obtained for the v5.1 and v4.6 XCH₄ datasets are shown in Table 7. The direction of change suggests regression in the mean bias and improvement in both accuracy and stability. However, as discussed in Sect 2.3 for XCO₂, a comparison between the estimates of these parameters obtained for the different dataset versions is not straightforward, and also in this case, the changes are considered as non-significant, thereby showing consistency in the estimated parameters for both versions of the datasets. Also in this case, due to their different spatial resolution, a direct comparison of the random error estimates does not reflect a change in the performance of the datasets.

Table 7: Comparison of  random error, accuracy and stability estimates obtained from v5.1 and v4.6 XCH₄ datasets, together with an assessment of their corresponding change.

Climate Change Assessment

The OBS4MIPS products analysed in this PQAR are merged products that were generated using various retrieval algorithms and satellite instruments (see Figures 2 and 3 in ATBD XGHG, 2026). Although the data were homogenized (ATBD XGHG, 2026), there may still be differences between the data products used, e.g. in sampling and data quality. This can lead to temporal or spatial changes in the characteristics of the OBS4MIPS products, which are, however, reflected in the estimated uncertainties.

In climate applications, it is important that the time series of a parameter studied are significantly longer than the changes caused by its natural variability. For this reason, climate scientists usually analyse at least several decades. The data analysed in this PQAR span more than two decades and the user requirements for stability are also met.

The OBS4MIPS data were thoroughly validated with co-located TCCON data in this PQAR. However, due to the spatially sparse sampling of TCCON sites (Figure 4), some geophysical conditions are underrepresented in the validation. These include, for example, deserts, some tropical regions and the southern hemisphere in general. Figures 2-5 of PUGS XGHG, 2026 show that the uncertainty (mainly due to the scatter between algorithms) is the highest in some of these regions.

In the past, predecessor versions of the OBS4MIPS data have already been used for climate applications such as the annual Copernicus ESOTC (European State of the Climate) Greenhouse Gas Concentrations report (ESOTC GHG, 2023). These data products were used to compute global average annual  atmospheric concentrations and their year-to-year increase for both CO₂ and CH₄.

Application(s) specific assessments 

Previous versions of the OBS4MIPS XCO₂ product have also been used in the peer-reviewed publications of Lauer et al. (2017) and Gier et al. (2020). In these publications, the OBS4MIPS XCO₂ products were used to evaluate the performance of CMIP (Coupled Model Intercomparison Project) models in its Phase 5 (CMIP5) in Lauer et al. (2017), and both its Phases 5 and 6 (CMIP5 and CMIP6) in Gier et al. (2020).

Quality assessments performed by C3S quality control evaluators on previous versions of the OBS4MIPS XCO₂ and XCH₄ products are also available here.

Compliance with user requirements concerning data quality

This section summarizes the achieved data quality including comparisons with the required data quality.

The development of satellite-derived gridded ECV products in OBS4MIPS format started in the framework of ESA’s Climate Change Initiative (CCI) via the GHG-CCI project (last access: 3-Mar-2025) (see also Reuter et al., 2020). The envisaged main application is comparison with climate models. The GHG-CCI project team therefore proposed several years ago to generate XCO₂ and XCH₄ products in OBS4MIPS format at the described spatio-temporal resolution. That spatio-temporal resolution was assumed to be appropriate for climate model comparisons considering also the characteristics of existing satellites. It was later confirmed by scientific studies (e.g., Lauer et al., 2017, and Gier et al., 2020) that the generated products were in fact very useful for the envisaged application.

Table 8 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 in Section 5.1 for XCO₂ and Section 5.2 for XCH₄ (see Section 1.3 for a more detailed explanation on the reported probabilities). 

Table 8: 

Summary data quality Level 3 XCO₂ product

Summary data quality Level 3 XCH₄ product

Acknowledgements

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