Last modified on Sept 10, 2025 08:46

Contributors: C. Crevoisier (Laboratoire de Météorologie Dynamique (LMD)/CNRS), N. Meilhac (FX-CONSEIL/Laboratoire de Météorologie Dynamique (LMD))

Issued by: Laboratoire de Météorologie Dynamique/CNRS, France

Date: 19/11/2024

Ref:  C3S2_313a_DLR_WP1-DDP-GHG-v1_PQAR_MTCO2_v10.1_MTCH4_v10.2

Official reference number service contract: 2024/C3S2_313a_DLR/SC1  

History of modifications

List of datasets covered by this document

Acronyms

General definitions

Essential climate variable (ECV): An ECV is a physical, chemical, or biological variable or a group of linked variables that critically contributes to the characterization of Earth's climate (Bojinski et al., 2014).

Climate data record (CDR): The US National Research Council (NRC) defines a CDR as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change (National Research Council, 2004).

Fundamental climate data record (FCDR): A fundamental climate data record (FCDR) is a CDR of calibrated and quality-controlled data designed to allow the generation of homogeneous products that are accurate and stable enough for climate monitoring.

Thematic climate data record (TCDR): A thematic climate data record (TCDR) is a long time series of an essential climate variable (ECV) (Werscheck, 2015).

Intermediate climate data record (ICDR): An intermediate climate data record (ICDR) is a TCDR which undergoes regular and consistent updates (Werscheck, 2015), for example because it is being generated by a satellite sensor in operation.

Satellite data processing levels: The NASA Earth Observing System (EOS) distinguishes six processing levels of satellite data, ranging from Level 0 (L0) to Level 4 (L4) as follows (Parkinson et al., 2006).

Absolute systematic error or systematic error: Component of measurement error that in replicate measurements remains constant or varies in a predictable manner. Note that "systematic error" refers to the absolute systematic error (in contrast to "relative systematic error" defined below). For satellite GHG ECV products especially the relative systematic error is important.

Relative systematic error, relative accuracy or relative bias: Identical with "Systematic error" but after bias correction and without considering a possible global offset (overall mean bias). Reflects the importance of spatially and temporally correlated errors (spatio-temporal biases). Computed from standard deviations of spatial and temporal biases. 

Bias: Estimate of a systematic measurement error.

Precision: Measure of reproducibility or repeatability of the measurement without reference to an international standard so that precision is a measure of the random and not the systematic error. Suitable averaging of the random error can improve the precision of the measurement but does not establish the systematic error of the observation (CMUG-RBD, 2012).
Note: Precision is quantified with the standard deviation (1-sigma) of the error distribution.

Stability: Term often invoked with respect to long-term records when no absolute standard is available to quantitatively establish the systematic error - the bias defining the time-dependent (or instrument-dependent) difference between the observed quantity and the true value (CMUG-RBD, 2012).
Note: Stability requirements cover inter-annual error changes. If the change in the average bias from one year to another is larger than the defined values, the corresponding product does not meet the stability requirement.

Representativity: Extent to which an average of a set of measured values corresponds to the true average, e.g., over a grid cell. It is important when comparing with or assimilating in models. Measurements are typically averaged over different horizontal and vertical scales compared to model fields. If the measurements are smaller scale than the model it is important. The sampling strategy can also affect this term (CMUG-RBD, 2012).

Threshold requirement: The threshold is the limit at which the observation becomes ineffectual and is not of use for climate-related applications (CMUG-RBD, 2012).

Goal requirement: The goal is an ideal requirement above which further improvements are not necessary (CMUG-RBD, 2012).

Breakthrough requirement: The breakthrough is an intermediate level between the "threshold" and "goal" requirements, which - if achieved - would result in a significant improvement for the targeted application. The breakthrough level may be considered as an optimum, from a cost-benefit point of view when planning or designing observing systems (CMUG-RBD, 2012).

Horizontal resolution: Area over which one value of the variable is representative of (CMUG-RBD, 2012). 

Vertical resolution: Height over which one value of the variable is representative of. Only used for profile data (CMUG-RBD, 2012). 

Observing Cycle (or Revisit Time): Temporal frequency at which the measurements are required (CMUG-RBD, 2012).

Averaging kernel: Vertical sensitivity of the retrieval to greenhouse gas mixing ratios.

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 the quality for two satellite-derived atmospheric carbon dioxide (CO₂) and methane (CH₄) C3S data products, v10.2 MTCO2_OBS4MIPS and v10.1 MTCH4_OBS4MIPS. These two products are mid-tropospheric-averaged air mixing ratios (mole fractions) of CO₂ and CH₄ products from observations made by Infrared Atmospheric Sounding Interferometer (IASI) and Advanced Microwave Sounding Unit-A (AMSU-A) instruments onboard the European Metop-A (July 2006-August 2021), Metop-B (since February 2013) and Metop-C (since May 2019) platforms (for more details, see MTGHG ATBD, 2024). The IASI hyperspectral observations in the thermal infrared at 7.7 µm for CH₄ and at 15 µm for CO₂ are sensitive to both temperature and gas concentrations of CH₄  / CO₂. These are used in conjunction with microwave observations form the AMSU-A instrument, which is only sensitive to temperature. These AMSU-A observations are used to decorrelate temperature variations from CO₂/CH₄ variations in the infrared radiance detected by IASI (Crevoisier et al., 2009a, 2009b, 2013).

The MTCO2_OBS4MIPS and MTCH4_OBS4MIPS products are merged multi-sensor MT-CO₂ and MT-CH₄ Level 3 (L3) products with daily time and 1^ox1^o spatial resolution generated using all available individual satellite sensor Level 2 (L2) products from Metop-A, -B and -C.

Validation is performed over a full suite of reference data: mixing ratios measured by aircraft, as well as mixing ratio profiles acquired by balloon-borne AirCore air samplers. Among these data, although limited to a few years, only the latter allows for a full validation of the mid-tropospheric column that can be derived from IASI observation. The aircraft network is used to evaluate long-term trends and latitudinal variations of the products. 

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.

1. Product validation methodology

Validation against high precision / low systematic errors reference observations is required for the mid/upper troposphere CO₂ and CH₄ data products. Unfortunately, measurements of both gases in the free troposphere and stratosphere are very sparse. Validation thus mostly relies on existing surface, aircraft and airborne measurements.

1.1. Description of reference data used for validation

1.1.1. Balloon-borne atmospheric samplers: AirCores

Balloon-borne air samplers AirCore give access to 0-30 km profiles of atmospheric mixing ratios of both CO₂ and CH₄ (Karion et al., 2010; Membrive et al., 2017). Averaging kernels can be applied to derive columns that can then be compared to those derived from space-borne observations. So far, only a few hundred profiles have been acquired, all in the northern hemisphere. In this validation exercise, use is made of CH₄ profiles extrapolated with the Copernicus Atmosphere Monitoring Service's (CAMS) profiles (version reanalysis “hb0k”) from all stations operated by European teams for which data are available: three stations located in France where monthly measurements are made in the framework of the French AirCore program (Aire-sur-l'Adour, Trainou, Reims), and two stations also managed by the French AirCore team (Timmins, Ontario, Canada and Kiruna in Sweden). Additional profiles acquired through a cooperation with the Finnish Meteorological Institute come from Sodankylä. Spanning 2014-2023, they are used to validate Metop-A, Metop-B and Metop-C retrievals. An example of AirCore methane profile is given in Figure 1 and the site locations are shown in Figure 2.

L3 MT-CO₂ Obs4MIPs products cover tropical airmasses, typically between 30°S to 30°N. Regular AirCore measurements are performed mostly over mid-latitudes in the Northern hemisphere where MT-CO₂ retrievals are not available. That is why there is no comparison between AirCores and MT-CO₂.

For the comparison, all L3 MT-CH₄ products falling in a 5°x5° grid cell centered on each AirCore profile for the same day are averaged. For that, the averaging kernel of each selected L3 MT-CH₄ is applied on the CH₄ profiles provided by the AirCore to obtain an AirCore IASI-like MT-CH₄. The averaging kernels are defined on the Thermodynamic Initial Guess Retrieval (TIGR) pressure grid (provided in the L3 MTGHG_OBS4MIPS netcdf files, see MTGHG PUGS, 2024 for more details). The CH₄ profiles provided by the AirCores are linearly interpolated as a function of pressure altitude on the TIGR pressure grid used in the retrieval. Thus, the CH₄ profiles of the AirCores and the averaging kernels are defined on the same pressure grid. To obtain the IASI-like MT-CH₄ from the AirCore measurements, we apply the following formula (Crevoisier et al., 2009b):

Where:

    ● \( MTCH4^{IASI-like}_{AirCore} \) is the mid-tropospheric column of CH₄ obtained using the AirCore CH₄ profile and the vertical sensitivity of the L3 MT-CH₄; 

    ● \( F_i \) is the value of the averaging kernel in the layer  \( i \) ;

    ● \( ∆p_i \) is the layer thickness in terms of pressure;

    ● \( X_{i,ch4} \)  is the value of the CH₄ mixing ratio provided by the AirCore measurement;

    ● \( Nlayer \) is the number of layers in the TIGR database and equal to 42 layers;

Figure 1: An example of a methane profile (purple line) extrapolated with CAMS 'hb0k' (green line), from AirCore launched at Reims, France and the typical averanging kernel of MT-CH₄ (black line).

Figure 2: Location of launching sites of AirCore used in the validation. Most of the measurements come from the 3 French sites that form the AirCore-Fr network.

1.1.2. Aircraft: CONTRAIL

Additional validation data come from measurements performed by commercial aircraft made as part of the CONTRAIL project (Matsueda et al. 2008, Machida et al., 2008, Sawa et al., 2015). Mixing ratios of CO₂ and CH₄ are provided at the altitude of the flights, typically at 10-12 km for most of the flight, and as profile during ascent or descent at airports. The current dataset spans the period April 1993 to March 2022. Note that only CONTRAIL data after July 2007 are used in the validation as this is when the MT-CH₄/CO₂ Obs4MIPs data became available. Figure 3 shows the CO₂ (Figure 3(a)) and CH₄ (Figure 3(b)) measurement points above 10 km per year between 2007 and 2022. These observations, partly analyzed by Matsueda et al. (2002), are available on a monthly basis. They cover the altitude range 9–13 km. Several gaps have affected the measurements throughout the period, which prevents making robust statistics from them.

Figure 3: Trajectories of CONTRAIL measurement of CO₂ (a) and CH₄ (b) above 9 km and per year between 2007 and 2022

The CONTRAIL measurements used for validation are those carried out between 9 and 13 km altitudes, i.e. at the altitude where the sensitivities to CO₂ and CH₄ of MT-CO₂ and MT-CH₄ products are maximum. These measurements are then averaged in 1°X1° grids and per month to obtain monthly L3 CONTRAIL products.   

In the following, CONTRAIL L3 CO₂ data and IASI L3 Obs4MIPs MT-CO₂ v10.1 are compared over 12 latitudinal bands of 5° each. CONTRAIL L3 CH₄ data and IASI L3 Obs4MIPs MT-CH₄ v10.2 are compared over 12 latitudinal bands of 5° each.

1.2. Validation methodology

1.2.1. Determination of the accuracies

To determinate the accuracy of MT-CO2_OBS4MIPS and MT-CH4_OBS4MIPS, we use the CONTRAIL measurements above 9 km, where the MT-CO₂ sensitivities to CO₂ are maximum. L3 CONTRAIL monthly means are compared with MT-CO2_OBS4MIPS monthly means in each 5° latitude band.  

The "relative systematic errors” are the standard deviations of the mean per-latitudinal 10° band bias between 40°S and 60°N for MT-CH₄ Obs4MIPs and per-latitudinal 5° band bias between 30°S and 30°N for MT-CO₂ Obs4MIPs, computed over the whole time series and the “relative spatio-temporal bias” is the standard deviation of the seasonal mean bias in each latitudinal band (i.e. JFM, AMJ, JAS, OND). 

For MT-CH4_OBS4MIPS, another validation over Mid-latitudes is performed using the AirCore CH₄ profiles (presented in section 1.1.1). All MT-CH₄ Obs4MIPs retrievals falling in a 5°x5° grid cell centered on each AirCore profile for the same day are averaged. For each couple of MT-CH4_OBS4MIPS and AirCores, the averaging kernel is applied to the CH₄ AirCore profile (Eq. 1) to obtain the mid-tropospheric column of CH₄ noted as, \( MTCH4^{IASI-like}_{AirCore} \) . We obtain the differences IASI-like MT-CH₄ from AirCore and MT-CH4_OBS4MIPS as:

where:

● \( \Delta^{AirCore} \) if the averaged difference of MT-CH₄ Obs4MIPs and IASI-like MT-CH₄ from the AirCore;
● \( MTCH4^{i,IASI-like}_{AirCore} \) is the mid-tropospheric column of CH₄ obtained with the application of the averaging kernel of the \( MTCH4^{i}_{Obs4MIPs} \)  to the AirCore CH₄ profile;

● \( N_{coloc} \) is the number of co-location of MT-CH₄ Obs4MIPs and the AirCore profile (same day, and in a 5°x5° grid cell centered around the AirCore profile);

The difference between MT-CH₄ Obs4MIPs and AirCores are given as the mean and the associated standard deviation of  \( \Delta^{AirCore} \) over the 81 AirCores currently available. This standard deviation defines the "random error" of MT-CH₄ Obs4MIPs. We used AirCores to define the "random error" rather than aircraft measurements because AirCores allow comparisons of the mid-tropospheric columns of CH₄ while the aircraft comparison compares mid-tropospheric columns of CH₄ (MT-CH4_OBS4MIP) with measurement points around 9-10 km altitudes.

1.2.2. Determination of the stability

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

For MT_CO2_OBS4MIPS: We assume that CONTRAILS measurements are stable and accurately represent the evolution of CO₂ in the mid-tropospheric region. From the co-located CONTRAIL and MT-CO2 _OBS4MIPS data, we calculate the associated time series per 5° latitude bands. From these time series, we infer trends using a linear fit and by latitude band. The goal of working by latitude band is to demonstrate whether stability is verified in each latitude band.

For MT-CH4_OBS4MIPS, due to several gaps in the time series of CONTRAIL, it is not possible to compute the stability.

Stability is then defined as the difference in trend between CONTRAIL and MT-CO2_OBS4MIPS. 

1.2.3. Limitation of validation

In the tropics, there are currently no independent validation sources that provide CO₂/CH₄ profiles between the surface and the lower stratosphere. Having regular AirCore launches in any tropical region would greatly improve the validation of IASI products as well as benefit the evaluation of model simulations. Therefore, we used CONTRAIL aircraft flights to determine the stability and accuracy of the MT-CO₂ Obs4MIPs and MT-CH₄ Obs4MIPs products. The limitations with CONTRAIL measurements are:

• CONTRAIL measurements do not cover all latitudes and longitudes;
• The time series of CONTRAIL measurements is impacted by several gaps in time coverage;
• These measurements don't provide a CO₂ profile but rather measurement points in the mid-troposphere; 

The AirCore measurements are used to validate MT-CH₄ Obs4MIPs products over northern mid-latitudes. The limitation here is the number of AirCores and no AirCore is currently available in tropics and southern mid-latitudes. 

The last limitation is that we don't have an independent source of validation of CH₄ profiles in the mid-troposphere and over the southern mid-latitudes.

2. Validation results

2.1. Validation results for Level 3 Obs4MIPs MT-CO₂ product

2.1.1. Validation with aircraft measurements 

The validation of Level 3 Obs4MIPs MT-CO₂ product is performed using the CONTRAIL aircraft measurement described in Section 1.1.2.

Figure 4 shows comparison of IASI L3 MT-CO₂ with L3 CONTRAIL aircraft data as monthly means in 12 latitudinal bands of 5° each. Figure 5 shows the scatter plot of IASI L3 MTCO2 vs. L3 CONTRAIL CO₂ for the whole period. The R correlation coefficient is 0.99, the bias and the standard deviation of the difference between them being 0.60 ± 1.25 ppm.

Figure 4: (a) Monthly mean of L3 IASI MT-CO₂ Obs4MIPs v10.1 (dashed line) from July 2007 to November 2022 and of L3 CONTRAIL CO₂ (full line) in 12 latitudinal bands of 5° each from 30°S to 30°N; (b) the associated differences between MT-CO₂ Obs4MIPs v10.1 and L3 CONTRAIL CO_2.

Figure 5: Scatter plot of L3 IASI mid-tropospheric CO₂ v10.1 vs. L3 CONTRAIL CO₂ measured at 10 km (1511 points of comparison) over the whole period available for CONTRAIL depicted in Fig. 4 (a) for measurements by aircraft at 10-12 km (Fig. 4 (a), dashed line) in 12 latitudinal bands of 5° each.

To compute the various parameters summarized in the following tables, the time series in each latitudinal band displayed in Figure 4 (a) have been used separately.

Table 1 shows the mean L3 CONTRAIL – L3 IASI Obs4MIPs MT-CO₂ difference together with the associated standard deviation recorded in each latitudinal band. The mean bias over all latitudinal band is 1.07 ppm. It comes down to 0.58 ppm when we restrict to 25S:25N, where most of the reference data are available.

Table 1: Mean and standard deviation (std) of CO₂ (ppm): difference between L3 CONTRAIL and IASI L3 Obs4MIPs MT-CO₂ v10.1 over 12 latitudinal bands of 5° each. Statistics over July 2007-November 2022

The relative systematic error is computed as the standard deviation of the L3 CONTRAIL – L3 MT-CO₂ bias obtained in each latitudinal band. It is computed as three values:

• The “systematic error”, which is the standard deviation of the mean per-latitudinal band bias computed over the entire time series. This was found to be 1.07 ppm.
• The “relative spatio-temporal bias”, which is the standard deviation of the seasonal mean bias in each latitudinal band (i.e. JFM, AMJ, JAS, OND). This was found to be 1.55 ppm.
• The “relative spatio-temporal error”, which is the standard deviation of the mean bias in each latitudinal band over the whole time series. This was found to be 1.42 ppm between 30°N:30°S and 0.52 between 20°S and 20°N.

For each latitudinal band, the linear drift was computed as the slope of the linear regression of the mean L3 CONTRAIL – IASI L3 MT-CO₂ bias against time. Table 2 shows the resulting drift. The main drift, that define the stability,  over all bands is 0.005 ± 0.04 ppm/year.

Table 2: Linear drift of CO2 (ppm/year)

2.2. Validation results for Level 3 Obs4MIPs MT-CH₄ product

For L3 Obs4MIPs MT-CH₄ products, only two quantities have been evaluated so far: single measurement precision, and mean bias with both aircraft and AirCore measurements. Due to limited time series of both aircraft and balloons, it has not yet been possible to evaluate the stability criteria.

2.2.1. Validation with aircraft measurements

 L3 Obs4MIPs MT-CH₄ are compared with measurements made in the framework of the CONTRAIL project (Machida et al., 2007, 2008; Matsueda et al., 2008; Sawa et al., 2015). All L3 Obs4MIPs MT-CH₄ falling in a 5°x5° grid cell centered on each L3 CONTRAIL measurement are averaged. The larger difference between the partial column retrieved from IASI and the in-situ concentration measured at 10-12 km by CONTRAIL is due to a larger vertical variability for CH₄ than for CO₂, hence, the comparison between satellite weighted columns and aircraft point measurements is expected to be less satisfactory for CH₄. Figure 6 shows the scatter plot of each pair of CONTRAIL / L3 Obs4MIPs MT-CH₄.

Over the whole dataset, the difference between L3 CONTRAIL and L3 Obs4MIPs MT-CH₄ is -1.81 ± 17.3 ppb, with a correlation R factor of 0.84.

Note that due to the many gaps in the CONTRAIL CH₄ time series, it is not possible to calculate the stability of MT-CH₄ using CONTRAIL airborne measurements.

Figure 6: L3 CONTRAIL CH4 vs. L3 Obs4MIPs MT-CH₄ for all CONTRAIL measurements over July 2007-November 2022 (701 points of comparison). The 1x1 line is shown as blue.

Figure 7 shows the monthly evolution of CH₄ as measured by CONTRAIL (dashed lines) and retrieved by IASI (full line) for 10 latitudinal bands of 10° each. The monthly evolution observed on both datasets is consistent whatever the latitude is, both in terms of seasonality and amplitude. Table 3 summarizes the statistics (mean and standard deviation) obtained within each 10 latitudinal bands for IASI, CONTRAIL and the difference between both. Both datasets are statistically in agreement. The standard deviations of IASI and CONTRAIL inside a given latitudinal band are noticeably close to each other.

Figure 7: (a) Comparison between CONTRAIL and L3 MT-CH₄ over July 2007-November 2022. Monthly evolution of L3 CONTRAIL CH₄ (dashed line) and IASI L3 MT-CH₄ (full line) for 8 latitudinal bands of 10° each. Each series is shifted by 30 ppb (black arrow) to be displayed on the same figure. (b) the associated differences between MT-CH₄ Obs4MIPs v10.2 and L3 CONTRAIL CH_4.

Table 3: Means and standard deviations of: 5°X5° gridded L3 CONTRAIL aircrafts (1^st line), L3 MT-CH₄ Obs4MIPs v10.2 (2^nd line), the differences L3 CONTRAIL - L3 MT-CH₄ (3^rd line) and the number of matchups (4^th line) over 10 latitudinal bands of 10° each. Statistics over July 2007-November 2022.

From Table 3, it is straightforward to compute the “relative spatial bias” of the “relative systematic error”, which is the standard deviation of the mean per-latitudinal band bias computed over the whole time series. The “relative systematic error”, which is the standard deviation of the mean bias in each latitudinal band over the whole time series, is found to be 3.80 ppb. Due to several gaps in the time series, as can be seen in Figure 7 (a), it is not possible to compute the “relative spatio-temporal bias” which is the standard deviation of the seasonal mean bias in each latitudinal band (i.e. JFM, AMJ, JAS, OND).  

2.2.2. Validation with AirCore 0-30 km profiles

Here, IASI Obs4MIPs MT-CH₄ products are compared to several AirCore profiles (see Section 1.1.1). Figure 8 shows the scatter plot of each pair of AirCore/ L3 Obs4MIPs MT-CH₄. Over the whole dataset (81 pairs), the difference between AirCore and IASI CH₄ is -4.5 ± 17.5 ppb.

Figure 8: Comparison between IASI Obs4MIPs MT-CH₄ v10.2 and AirCore CH4. Correlation is 0.82.

3. Climate Change Assessment (to be implemented progressively)

In this section, reports on the output of the Climate Intelligence activities will be included as they become available.

4. Application(s) specific assessments 

The v10.2 MT-CO2_OBS4MIPS and v10.1 MT-CH4_OBS4MIPS products validated in this document have not yet been used for application specific assessments in terms of peer-reviewed publications. 

5. Compliance with user requirements concerning data quality

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

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.

The TRD GAD GHG, 2024, document contains explicit requirements for random errors, systematic errors and stability of the Level 2 MTCO₂ and MTCH₄ data products in terms of goal (G), breakthrough (B)and threshold (T) requirements. Explicit requirements for Level 3 products are not formulated in TR-GAD GHG, 2024. Instead, it is assumed that the accuracy and stability requirements are also valid for Level 3 (i.e., spatio-temporally averaged) data products.

As explained in Section 2 of TR-GAD GHG, 2024, the GCOS requirements as formulated in GCOS-245, are not applicable for the data products as presented in this document as these new GCOS requirements are formulated for future missions (e.g., CO2M) and are not appropriate for existing satellite sensors are used for this project. The following is written in TR-GAD GHG, 2024: “Because these new requirements are for future missions, we use in this document (wherever possible) the requirements as have been formulated by the Climate Research Group (CRG) of the GHG-CCI project of ESA’s Climate Change Initiative. We use the latest version, which is the User Requirements Document (URD) referred to ESA-CCI-GHG-URD, 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 in Section 5.1 for MT-CO₂ and Section 5.2 for MT-CH₄.

Table 4: Compliance with User Requirements. MT-CO₂ and MT-CH₄ Obs4MIPs random (“precision”), systematic error and stability requirements (from TRD GAD GHG, 2024). Abbreviations: G=Goal (green), B=Breakthrough (yellow), T=Threshold requirement (red). ^§) Required systematic error after an empirical bias correction, that does not use the verification data. ^#) Required systematic error and stability after bias correction, where bias correction is not limited to the application of a constant offset / scaling factor

5.1.  Summary data quality Level 3 MT-CO₂ product

The validation of Level 3 product MTCO2_OBS4MIPS can be summarized as follows:

• The overall monthly mean uncertainty is 1.25 ppm and the mean bias is 0.6 ppm. This value is based on the comparison between partial column (MT-CO₂) and point measurement (Aircraft measurements).
• Relative systematic error, i.e., the spatio-temporal error, is 1.42 ppm (1-sigma) between 30°S:30°N and 0.52 ppm (1-sigma) between 20°S and 20°N. These values are based on the comparison between partial column partial column (MT-CO₂) and point measurement (Aircraft measurements). The computed linear drift of 0.005±0.04 ppm (1-sigma) is small and not significant.
• Overall, this product has therefore reasonable accuracy and high stability.

5.2. Summary data quality Level 3 MT-CH₄ product

The validation of Level 3 product MTCH4_OBS4MIPS can be summarized as follows:

• The overall monthly mean uncertainty is 17.3 ppb and the mean bias is -1.81 ppb. Relative systematic error, i.e., the spatio-temporal error is 3.80 ppb (1-sigma).
• Overall, this product has therefore reasonable accuracy.

Acknowledments

We acknowledge previous funding by the European Space Agency (ESA) via Climate Change Initiative (CCI) project GHG-CCI. This funding enhanced the quality of the retrieval algorithms and related documentation. This resulted in more mature data products as needed for an operational project such as the Copernicus Climate Change Service (C3S). We also acknowledge the ESPRI computing center.
The development of retrieval algorithms based on IASI observations would have not been possible without the strong support of CNES.
We thank the AirCore-Fr team for providing AirCore data and CONTRAIL team for providing aircraft measurements of greenhouse gases.

References

Bojinski et al., 2014: Bojinski, S., M. Verstraete, T.C. Peterson, C. Richter, A. Simmons, M. Zemp, The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy. Bull. Amer. Meteor. Soc., 95, 1431–1443. Available at http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-13-00047.1, 2014.

 CMUG-RBD, 2012: Climate Modelling User Group Requirements Baseline Document, Deliverable 1.2, Number D1.2, ESA Climate Change Initiative (CCI), Version 2.0, 17 Dec 2012, 2012. Link: https://climate.esa.int/media/documents/CMUG_D1.2_URD_v2.0.pdf

Crevoisier et al., 2009a: Crevoisier, C., Chédin, A., Matsueda, H., et al., First year of upper tropospheric integrated content of CO₂ from IASI hyperspectral infrared observations, Atmos. Chem. Phys., 9, 4797-4810, 2009. https://doi.org/10.1029/2001JD001443 

Crevoisier et al. 2009b: Crevoisier, C., Nobileau, D., Fiore, A., Armante, R., Chédin, A., and Scott, N. A.: Tropospheric methane in the tropics – first year from IASI hyperspectral infrared observations, Atmos. Chem. Phys., 9, 6337–6350, doi:10.5194/acp-9-6337-2009,  https://doi.org/10.5194/acp-9-6337-2009, 2009.

Crevoisier et al., 2013: Crevoisier, C., Nobileau, D., Armante, R., et al., The 2007–2011 evolution of tropical methane in the mid-troposphere as seen from space by MetOp-A/IASI, Atmos. Chem. Phys., 13, 4279-4289,  https://doi.org/10.5194/acp-13-4279-2013, 2013.

ESA-CCI-GHG-URD, 2024: Chevallier, F., Marshall, J., et al., User Requirements Document (URD) for the Essential Climate Variable (ECV) Greenhouse Gases (GHG), ESA Climate Change Initiative (CCI) GHG-CCI+ project, Version 4.0, 16-May-2024, pp. 40, 2024. Link: https://www.iup.uni-bremen.de/carbon_ghg/docs/GHG-CCIplus/URDv4/URDv4.0_GHG-CCI+.pdf

ESTOC GHG, 2023: European State of the Climate: Greenhouse gas concentrations, compiled by the Copernicus Climate Change Service (C3S) and the World Meteorological Organization (WMO). Link: https://climate.copernicus.eu/esotc/2023/greenhouse-gas-concentrations

GCOS-154: Global Climate Observing System (GCOS): SYSTEMATIC OBSERVATION REQUIREMENTS FOR SATELLITE-BASED DATA PRODUCTS FOR CLIMATE - 2011 Update - Supplemental details to the satellite-based component of the "Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update)", December 2011, prepared by World Meteorological Organization (WMO), Intergovernmental Oceanographic Commission, United Nations Environment Programme (UNEP), International Council for Science, Doc.: GCOS 154, link: http://cci.esa.int/sites/default/files/gcos-154.pdf, 2011.

GCOS-195: Status of the Global Observing System for Climate, GCOS-195, 2015. Link: https://library.wmo.int/doc_num.php?explnum_id=7213

GCOS-200: The Global Observing System for Climate: Implementation Needs, World Meteorological Organization (WMO), GCOS-200 (GOOS-214), pp. 325. Link: http://unfccc.int/files/science/workstreams/systematic_observation/application/pdf/gcos_ip_10oct2016.pdf, 2016.

GCOS-245: The 2022 GCOS ECVs Requirements, WMO, pp. 244, 2022.  Link: https://library.wmo.int/doc_num.php?explnum_id=11318

Jacquinet-Husson et al. 2011: Jacquinet-Husson N., L. Crépeau, R. Armante, C. Boutammine, A. Chédin, et al.. The 2009 edition of the GEISA spectroscopic database, J. Quant. Spectrosc. Radiat. Transfer,112, 2395-2445 doi:10.1016/j.jqsrt.2011.06.004, https://doi.org/10.1016/j.jqsrt.2011.06.004, 2011. 

Machida et al. 2008: Machida, T., Matsueda, H., Sawa, Y., Nakagawa, Y., Hirotani, K., Kondo, N., Goto, K., Nakazawa, T., Ishikawa, K., and Ogawa, T.: Worldwide measurements of atmospheric CO₂ and other trace gas species using commercial airlines, J. Atmos. Ocean. Tech., 25(10), 1744–1754, doi:10.1175/2008JTECHA1082.1, 2008, https://doi.org/10.1175/2008JTECHA1082.1

Matsueda et al. 2008: Matsueda, H., Machida, T., Sawa, Y., Nakagawa, Y., Hirotani, K., Ikeda, H., Kondo, N., and Goto, K.: Evaluation of atmospheric CO₂ measurements from new flask air sampling of JAL airliner observation, Pap. Meteorol. Geophys., 59, 1–17, 2008, https://doi.org/10.2467/mripapers.59.1

Membrive et al. 2017: Membrive, O., Crevoisier, C., Sweeney, C., Danis, F., Hertzog, A., Engel, A., Bönisch, H., and Picon, L.: AirCore-HR: a high-resolution column sampling to enhance the vertical description of CH₄ and CO₂, Atmos. Meas. Tech., 10, 2163-2181, https://doi.org/10.5194/amt-10-2163-2017, 2017.

National Research Council, 2004: National Research Council, Climate Data Records from Environmental Satellites: Interim Report 2004, 150 pp., ISBN: 978-0-309-09168-8, DOI: https://doi.org/10.17226/10944, 2004.

Parkinson et al., 2006: Parkinson, C., A. Ward and M. King, Earth Science Reference Handbook, NASA, Washington DC, 2006.

PUGS MTGHG, 2024: Crevoisier, C., Meilhac, N., C3S Greenhouse Gas (GHG: MTCO2 v10.1 & MTCH4 v10.2): Product User Guide and Specification (PUGS)

Sawa et al. 2015: Sawa, Y., Machida, T., Matsueda, H., Niwa, Y., Tsuboi, K., Murayama, S., Morimoto, S., and Aoki, S.: Seasonal changes of CO₂, CH₄, N₂O, and SF₆ in the upper troposphere/lower stratosphere over the Eurasian continent observed by commercial airliner, Geophys. Res. Lett., 42, 2001–2008, https://doi.org/10.1002/2014GL062734, 2015.

Scott and Chédin 1981: Scott N.A. and Chédin A., A fast line-by-line method for atmospheric absorption computations: The Automatized Atmospheric Absorption Atlas, J. Appl. Meteor., 20, n° 7, pp. 802-812, 1981.  https://doi.org/10.1175/1520-0450(1981)020%3C0802:AFLBLM%3E2.0.CO;2

TR-GAD GHG, 2024: Buchwitz, M., et al., Target Requirement and Gap Analysis Document, Copernicus Climate Change Service (C3S) project on satellite-derived Essential Climate Variable (ECV) Greenhouse Gases (CO₂ and CH₄) data products (project C3S2_312a_Lot2), Version 5.6, 22-Mar-2024, pp. 87, 2024. Latest version: http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_TRD-GAD_GHG_latest.pdf

Werschek, 2015: Werschek, M., EUMETSAT Satellite Application Facility on Climate Monitoring, C3S Climate Data Store workshop, Reading, UK, 3-6 March 2015. http://www.ecmwf.int/sites/default/files/elibrary/2015/13546-existing-solutions-eumetsat-satellite-application-facility-climate-monitoring.pdf

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