EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
suitable to constrain global and regional Earth's carbon fluxes in "top-down" inversion systems.

![image.png](attachment:196b9642-4db0-4282-bb42-20bffbd84814.png)

*The figure shows the global carbon budget estimated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO$_2$ data and the global atmospheric growth rate by [[12]](https://doi.org/10.15138/9N0H-ZH07). $E_{\mathrm{FOS}}$ denotes fossil fuel CO$_2$ emissions, $E_{\mathrm{FIRE}}$ denotes biomass combustion emissions, $F_{\mathrm{OCEAN}}$ denotes ocean-atmosphere carbon fluxes, NEE denotes net ecosystem exchange (i.e. the balance of photosynthesis and respiration from the terrestrial ecosystem) and $G_{\mathrm{ATM}}$denotes the growth rate of atmospheric CO$_2$ concentration derived from GONGGA and [[12]](https://doi.org/10.15138/9N0H-ZH07). Image reproduced from [[7]](https://essd.copernicus.org/articles/16/2857/2024/) under CC-BY licence.*

## 📢 Quality assessment statement

These are the key outcomes of this assessment

* The XCO2_OBS4MIPS Level 3 product has been primarily generated for comparison with climate models and has also been used for computations of annual mean atmospheric growth rates. This data product in not recommended to derive global and regional carbon fluxes.
* In terms of spatial resolution, the mid-tropospheric-averaged air mole fractions of CO$_2$ (MTCO2_OBS4MIPS, Level 3) data product appears appropriate for use in global and regional inversion system. However, users must carefully consider limitations related with spatial completeness (mostly limited to tropical regions) and vertical representativeness. As there are no known applications of this MT-CO$_2$ product, extreme caution should be exercised to use this data product to derive global and regional carbon fluxes by inversion modelling systems.
* The deprecated Level 2 XCO$_2$ datasets can be used within inversion modelling systems to derive historical global and regional carbon fluxes. Users would use the data product derived by the algorithm XCO2_EMMA that optimise data availability over regions frequently affected by cloud cover or high aerosol loading.
* The spatial coverage and resolution (spatial density) of the satellite data or a combination of them, affected the impact of satellite observations on carbon flux quantifications in different regions.
* As the MTCO2_OBS4MIPS dataset is updated every six months and Level 2 XCO$_2$ won't be extended in time after 2022, users interested in more timely greenhouse gas fluxes quantification should consider other [Copernicus resources](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview).
```

## 📋 Methodology

To evaluate if the spatial coverage and resolution of satellite-based observations characterizing the Climate Data Store dataset “Carbon dioxide data from 2002 to present derived from satellite observations” [[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) are suitable for their usage in an atmospheric inversion system to investigate Earth's carbon fluxes, we inspected the related CDS Documentation [[14]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160575) as well as external references like [[15]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf), and case studies available from scientific literature ([[3]](https://doi.org/10.5194/acp-14-13739-2014), [[5]](https://acp.copernicus.org/articles/19/14233/2019/), [[6]](https://doi.org/10.5194/acp-19-12067-2019), [[7]](https://essd.copernicus.org/articles/16/2857/2024/)) and applications (e.g., [[8]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes), [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain)). Moreover, we performed specific analyses on the spatial data coverage of the Level 3 data product Obs4MIPs (version 4.6) and IASI-MERGED-OBS4MIPS (version 10.1).


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
)). Moreover, we performed specific analyses on the spatial data coverage of the Level 3 data product Obs4MIPs (version 4.6) and IASI-MERGED-OBS4MIPS (version 10.1).

In particular:
 * [[3]](https://doi.org/10.5194/acp-14-13739-2014) inferred regional estimates of the European land carbon sink by performing a regional surface flux inversion using earlier versions of the XCO$_2$ satellite measurements from SCIAMACHY (BESD v02.00.08 and) and GOSAT (ACOS v3.4r03 2010, UoLFP v4.0 2010, RemoTeC v2.11 2010, and NIES v02.xx 2010) than those available from the Climate Data Store dataset “Carbon dioxide data from 2002 to present derived from satellite observations” [[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview).

* [[5]](https://acp.copernicus.org/articles/19/14233/2019/) evaluated an ensemble of six multi-year global CO$_2$ atmospheric inversions by using a large dataset of accurate aircraft measurements in the free troposphere over the globe. For the inversion experiments, besides using OCO-2 data (ACOS bias-corrected retrievals, version 9), they also considered a previous version of a Level 2 data product available by the Climate Data Store (CO2_GOS_OCFP, v7.1).

* [[6]](https://doi.org/10.5194/acp-19-12067-2019) compared the results of constraining the terrestrial ecosystem carbon fluxes by assimilating GOSAT and OCO-2 XCO$_2$ retrievals (both produced by the "NASA Atmospheric CO$_2$ Observations from Space" project, version b7.3) within the GEOS-Chem 4D-Var assimilation framework.

* [[7]](https://essd.copernicus.org/articles/16/2857/2024/) presented a global spatially resolved terrestrial and ocean carbon flux dataset for years 2015–2022 generated by an atmospheric inversion system through the assimilation of OCO-2 XCO$_2$ retrievals (Level 2, Lite v11r data product).

* [[8]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes) provide a case about the use of CO$_2$ satellite observations to estimate net CO$_2$ fluxes into the atmosphere at global scale and over specific land and oceans regions by using the [CAMS](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain) global inversion systems. This inversion system uses in-situ atmospheric observations from large living databases, like [NOAA Earth System Research Laboratory Observation Package](https://www.esrl.noaa.gov/gmd/ccgg/obspack/), [Integrated Carbon Observation System](https://icos-atc.lsce.ipsl.fr/), and satellite retrievals from OCO-2.

Please note that the analysed references referred to the use of Level 2 data products within atmospheric inversion systems, for which there are example applications. Level 3 data products for satellite XCO$_2$ or MT-CO$_2$ have not yet been used in inversion systems to derive quantifications of surface CO$_2$ fluxes. However, a critical comparison of the resolution and completeness features of the Level 3 data products with respect to known applications based on Level 2 data products has been performed.

In the following, we report the outline of this notebook.

**[](template:section-1)**
 * Import all relevant packages.
 * Cache needed functions.

**[](template:section-2)**
 * Define the data request to CDS.

**[](template:section-3)**
 *  We compared the spatial resolution of the “Carbon dioxide data from 2002 to present derived from satellite observations” [[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) data products with those of the the satellite datasets used by atmospheric inversion experiments documented above.


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
(https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) data products with those of the the satellite datasets used by atmospheric inversion experiments documented above.

**[](template:section-4)**
 * We created plots of the monthly mean data coverage of the Level 3 data product Obs4MIPs (version 4.6) and IASI-MERGED-OBS4MIPS (version 10.1) and we compared them with those of the satellite datasets used by atmospheric inversion experiments documented above. Moreover, we also considered external documentation ([[15]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)) to assess the spatial coverage of the (deprecated) Level 2 data products part of the “Carbon dioxide data from 2002 to present derived from satellite observations” [[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) dataset.

**[](template:section-5)**
 * We provide a summary of the main outcomes of this assessment.

## 📈 Analysis and results

(template:section-1)=
### 1. Set-up the code and choose the data to use

#### Import all relevant packages
In this section, we import all the relevant packages needed for running the notebook.

#### Cache needed functions
In this section, we cached a list of functions used in the analyses.

* The `convert_units` function rescales XCO$_2$ mole fraction to parts per million (ppm).

* The `compute_coverage_obs4mips` function calculates the spatial fractional coverage of the available data for the Obs4MIPs Level 3 dataset.

* The `compute_coverage_iasi` and `aggregate_coverage_iasi` functions calculate the spatial fractional coverage of the available data for the IASI-MERGED-OBS4MIPS Level 3 dataset.

(template:section-2)=
### 2. Retrieve data

#### 2.1 Obs4MIPs
In this section, we define the data request to CDS (data product Obs4MIPs, Level 3, version 4.6, XCO$_2$) and download the full dataset.

#### 2.2 IASI-MERGED-OBS4MIPS
In this section, we define the data request to CDS (data product IASI-MERGED-OBS4MIPS, Level 3, version 10.1, MT-CO$_2$) and download the full dataset (please note that users can modify the time period).

Time parameters

(template:section-3)=
### 3. Spatial resolution assessment
Ideally, the spatial resolution of the satellite observations used in an inversion system should match that of the transport model. In turn, the transport model should match the spatial scale of carbon fluxes, which is typically on the order of hundreds of metres. In practice, however, contemporary models generally have much coarser resolutions, typically on the order of degrees ([[16]](https://acp.copernicus.org/articles/19/14233/2019/)), and errors in the simulated atmospheric transport may also affect the inversion results ([[17]](https://doi.org/10.1029/2005JD006390#jgrd12458-bib-0008)).


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
inversion results ([[17]](https://doi.org/10.1029/2005JD006390#jgrd12458-bib-0008)).

The documented use cases ([[5]](https://acp.copernicus.org/articles/19/14233/2019/), [[6]](https://doi.org/10.5194/acp-19-12067-2019),[[7]](https://essd.copernicus.org/articles/16/2857/2024/), [[8]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes)) used OCO-2 observations characterised by spatially dense data with a narrow swath (10.3 km) and with footprints of a few square kilometres (1.29 km × 2.25 km). [[5]](https://acp.copernicus.org/articles/19/14233/2019/) and [[6]](https://doi.org/10.5194/acp-19-12067-2019) also used a previous version of the CDS GOSAT Level 2 deprecated dataset, characterised by a coarser-resolution data (100 km$^2$ at nadir) with lower spatial density than OCO-2. A more recent application is the  inversion system that generates the CAMS global CO$_2$ atmospheric inversion product (PyVAR, see [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain)): it uses OCO-2 bias-corrected land retrievals of XCO$_2$ after averaging glint and nadir OCO-2 retrievals in 10-s bins (corresponding to a ground-track swaths of 67 km) to match the transport model horizontal spatial resolution of about 90 km$^{2}$.

* **XCO2_OBS4MIPS Level 3**: the spatial aggregation at 5° × 5° resolution and the temporal aggregation at monthly scales inevitably reduce the amount of information available to the inversion system. Such levels of aggregation are not commonly adopted in global or regional inverse modeling studies (see, e.g., [[5]](https://acp.copernicus.org/articles/19/14233/2019/), [[6]](https://doi.org/10.5194/acp-19-12067-2019), [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain)): indeed, this data product has primarily been generated for comparison with climate models and climate monitoring, rather than for the quantification of carbon fluxes through the ingestion-inversion system.

* **MTCO2_OBS4MIPS Level 3** : this dataset is new and thus never used before. However, Level 2 MT-CO2 and MT-CH4 products have been used for flux inversions ([[18]](https://acp.copernicus.org/articles/14/577/2014/), [[19]](https://acp.copernicus.org/articles/24/10639/2024/)) and the current spatial (1° x 1°) and temporal (1 day) resolution of this Level 3 data product can potentially meet the requirements for the investigation of carbon fluxes at regional and global scales (see, e.g. [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain)).


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
e.g. [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain)).

* **Level 2 (deprecated)**: GOSAT and GOSAT-2 measurements (CO2_GOS_SRFP v2.3.8, CO2_GOS_OCFP v7.3, CO2_GO2_SRFP v2.1.0) are characterised by a 9.7 km (diameter) footprint with the single observations typically on the order of 100 km apart with a 6-day repeat cycle. [[5]](https://acp.copernicus.org/articles/19/14233/2019/) and [[6]](https://doi.org/10.5194/acp-19-12067-2019) used previous versions of the GOSAT Level 2 deprecated dataset. According to [[5]](https://acp.copernicus.org/articles/19/14233/2019/), the lower data density of GOSAT observations with respect to OCO-2 may potentially affect the quantification of carbon fluxes but no conclusive statements were made on this topic. For use with an inverse modelling system, interested users may also consider the deprecated Level 2 merged multi-sensor XCO2_EMMA data product that consists of individual Level 2 soundings retrieved by different sensors (including also OCO-2) and algorithms ([[20]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf)).

(template:section-4)=
### 4. Spatial completeness assessment
The impact of satellite data on the carbon fluxes diagnosed by the inversion system is affected by to the spatial coverage and by the amount of data provided by the satellite measurements. In general, for regions characterised by low data coverage, the carbon fluxes diagnosed by the inversion system are dominated by the information provided by the "a-priori" fields (see [[6]](https://doi.org/10.5194/acp-19-12067-2019)). As documented by [[5]](https://acp.copernicus.org/articles/19/14233/2019/) and [[6]](https://doi.org/10.5194/acp-19-12067-2019), both OCO-2 and GOSAT observations are not evenly distributed spatially.

Due to cloud contamination, there are few retrievals in a large portion of tropical land. When clouds are present, they act as opaque or semi-opaque barriers to the solar radiation reflected at the Earth’s surface, meaning that the radiation reflected from the cloud top does not carry information about the gases below the clouds.

Moreover, available satellite retrievals are very sparse in the northern high-latitude area, especially in boreal regions, due to the occurrence of low solar zenith angles (SZAs). This is because high SZAs, which are typical of regions at high latitudes, complicate retrievals due to weaker illumination. This reduces the satellite radiance signal, making retrievals noisier and more uncertain. Furthermore, the long slant paths under high SZAs amplify the influence of aerosol scattering and cirrus clouds, which can result in errors in the retrieved gas amount. Furthermore, they cause multiple scattering and path-length uncertainty, which can affect satellite radiance observations in the near infrared/short wave infrared (NIR/SWIR) spectral region.
As reported by [[21]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577), the specific retrieval difficulties depend on the retrieval algorithm adopted.


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
As reported by [[21]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577), the specific retrieval difficulties depend on the retrieval algorithm adopted.

* **XCO2_OBS4MIPS Level 3**: this is a global dataset, obtained by gridding the merged Level 2 products generated with the ensemble median algorithm (XCO2_EMMA, see [[21]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577)). EMMA combines several different XCO$_2$ level-2 satellite data products: SCIAMACHY/Envisat (2003–2012), TANSO-FTS/GOSAT (2009–2022), OCO-2 (2014-2022), TANSO-FTS-2/GOSAT2 (2019-2023). The completeness of the spatial data varies with the seasons due to the above-described limitations in retrieving XCO$_2$ (i.e. low reflected sunlight during periods characterised by low SZAs, and surface shielding due to clouds). Data for the Southern Hemisphere (SH) mid and high latitudes is available from September to March, whereas data for the Northern Hemisphere (NH) mid and high latitudes is mostly available from April to August. Furthermore, the number of available observations depends significantly on location, with low coverage over areas typically affected by high cloud (such as the tropics).

```text
100%|██████████| 1/1 [00:00<00:00,  1.95it/s]
```

*The figure shows the monthly fractional XCO$_2$ data coverage from 2003 to 2023 derived from the XCO2_OBS4MIPS dataset (version 4.6). Please note that the colour scale has been set to optimise visualisation of the spatial path of the data coverage.*

* **MTCO2_OBS4MIPS Level 3**: as described in the Documentation [[22]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160640), the dataset is mostly limited to tropical air masses (between about 30°N and 30°S) due to the fact that decorrelation between CO$_2$ and temperature signals in the IASI radiances is more complex outside of the tropical region, yielding too high retrieval uncertainty. Moreover, it should be stressed that this is a mid‑troposphere product with the highest sensitivity of the measurements around 250 hPa (see [[28]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160673)), while surface CO$_2$ fluxes imprint their strongest signals in the boundary layer (0–2 km). This implies potential limitations in the ability of this dataset in constraining surface CO$_2$ fluxes.

```text
100%|██████████| 210/210 [01:31<00:00,  2.30it/s]
```

*The figure shows the monthly fractional XCO$_2$ data coverage from 2007 to 2024 derived from the IASI-MERGED-OBS4MIPS (version 10.1). Please note that the colour scale has been set to optimise visualisation of the spatial path of the data coverage.*

* **Level 2 (deprecated)**: with respect to spatial completeness GOSAT data products are characterised by low data coverage at high latitudes (due to unfavourable illumination conditions) and over the tropics (due to frequent cloud contamination). For use with an inverse modelling system to obtain information on CO$_2$ surface fluxes, interested users may also consider the Level 2 merged multi-sensor XCO2_EMMA data product ([[20]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf)) that optimises the spatial and the temporal data coverage by merging XCO$_2$ data from different sensors and algorithms. The XCO2_EMMA data product consists of individual Level 2 soundings retrieved by algorithms that may vary from gridbox to gridbox and from month to month. As recommended by [[23]](https://amt.copernicus.org/articles/13/789/2020/), it is important to note that these merged products are not necessarily the most optimal products for all applications, as they do not include all data from a given satellite sensor.

![Figure_GOS_XCO2.png](attachment:f1d30092-e175-41f4-b8c8-8e1a5b5e895d.png)


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
all data from a given satellite sensor.

![Figure_GOS_XCO2.png](attachment:f1d30092-e175-41f4-b8c8-8e1a5b5e895d.png)

*Figure showing global seasonal maps of GOSAT XCO2 "CO2_GOS_OCFP" retrieved between December 2021 and November 2022. XCO$_2$ values are expresssed as parts per million (ppm). Image adapted from [[24]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_A_latest.pdf) under CC-BY license.*

![Figure_GO2_XCO2.png](attachment:b763ef8a-3c34-4635-90a8-accdd2de12a6.png)

*Figure showing global XCO$_2$ map of GOSAT XCO2 "CO2_GO2_SRFP" for the 2019-2022 period on a 1° X 1° resolution for both land (top) and ocean (bottom) retrievals. XCO$_2$ values are expresssed as parts per million (ppm). Image adapted from [[25]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_B_latest.pdf) under CC-BY license.*

(template:section-5)=
### 5. How suitable are these datasets for ingestion into inversion systems?
In this section, we summarise the features of the "Carbon dioxide data from 2002 to present derived from satellite observations" dataset ([13](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview)) in terms of spatial resolution and completeness, in order to constrain the "top-down" inversion systems of global and regional Earth's carbon fluxes. Please note that Level 3 XCO$_2$ or MT-CO$_2$ satellite data products have not yet been used in inversion systems to derive surface CO$_2$ flux quantifications. Thus, the outcomes of this assessment, especially for the MT-CO2_OBS4MIPS data product, may change in the future if applications become available.

* **XCO2_OBS4MIPS Level 3**: This data product is characterised by temporal (monthly) and spatial (5° x 5°) resolutions higher that those typically used to quantify surface CO$_2$ fluxes by inversion systems. Also considering that no applications currently exist for using this XCO$_2$ data product, it cannot be recommended for quantifying surface CO$_2$ fluxes by inversion modelling systems.

* **MTCO2_OBS4MIPS Level 3**: This data product is characterised by temporal (daily) and spatial (1° x 1°) resolutions consistent with those reported in the scientific literature and use cases for use in an inversion system. The data product is primarily limited to tropical air masses (between approximately 30°N and 30°S), with the highest measurement sensitivity around 250 hPa. This implies potential limitations for this data product to constrain surface CO$_2$ fluxes. As there are currently no applications for using this MT-CO$_2$ data product, extreme caution should be exercised when using it to quantify surface CO$_2$ fluxes through inversion modelling systems.

*  **Level 2 (deprecated)**: these XCO$_2$ data products are suitable to be ingested in inversion modelling systems for the quantification of historical (2002 - 2022) surface CO$_2$ fluxes. In particular, users may consider the XCO2_EMMA data product that optimises the spatial and the temporal data coverage.

Please note that this assessment was not produced to rank the distinctive performance of the OCO-2 and GOSAT datasets in atmospheric inversion systems. Differences in the results of inversion experiments can be related to different factors, some of which are related to the satellite observations used (such as their data coverage, data precision, data accuracy including the implemented bias correction), while others can be related to other components of the inversion system (e.g. the transport model used, the "a-priori" field, the inversion method used).


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Carbon dioxide data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview

Users interested in accessing CO$_2$ fluxes from inversion systems can consider to use specifically designed Copernicus resources like [CAMS global inversion-optimised greenhouse gas fluxes and concentrations](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview).


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > References
---
[[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=) World Meteorological Organization. (2023). WMO Greenhouse Gas Bulletin, 19, ISSN 2078-0796.

[[2]](https://essd.copernicus.org/articles/17/965/2025/) Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Landschützer, P., Le Quéré, C., Li, H., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Arneth, A., Arora, V., Bates, N. R., Becker, M., Bellouin, N., Berghoff, C. F., Bittig, H. C., Bopp, L., Cadule, P., Campbell, K., Chamberlain, M. A., Chandra, N., Chevallier, F., Chini, L. P., Colligan, T., Decayeux, J., Djeutchouang, L. M., Dou, X., Duran Rojas, C., Enyo, K., Evans, W., Fay, A. R., Feely, R. A., Ford, D. J., Foster, A., Gasser, T., Gehlen, M., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A. K., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Kato, E., Keeling, R. F., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Lan, X., Lauvset, S. K., Lefèvre, N., Liu, Z., Liu, J., Ma, L., Maksyutov, S., Marland, G., Mayot, N., McGuire, P. C., Metzl, N., Monacci, N. M., Morgan, E. J., Nakaoka, S.-I., Neill, C., Niwa, Y., Nützel, T., Olivier, L., Ono, T., Palmer, P. I., Pierrot, D., Qin, Z., Resplandy, L., Roobaert, A., Rosan, T. M., Rödenbeck, C., Schwinger, J., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Steinhoff, T., Sun, Q., Sutton, A. J., Séférian, R., Takao, S., Tatebe, H., Tian, H., Tilbrook, B., Torres, O., Tourigny, E., Tsujino, H., Tubiello, F., van der Werf, G., Wanninkhof, R., Wang, X., Yang, D., Yang, X., Yu, Z., Yuan, W., Yue, X., Zaehle, S., Zeng, N., and Zeng, J. (2025). Global Carbon Budget 2024, Earth System Science Data, 17, 965-1039. https://doi.org/10.5194/essd-17-965-2025


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > References
---
). Global Carbon Budget 2024, Earth System Science Data, 17, 965-1039. https://doi.org/10.5194/essd-17-965-2025

[[3]](https://doi.org/10.5194/acp-14-13739-2014) Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Schneising, O., Pillai, D., Bovensmann, H., Burrows, J. P., Bösch, H., Parker, R., Butz, A., Hasekamp, O., O'Dell, C. W., Yoshida, Y., Gerbig, C., Nehrkorn, T., Deutscher, N. M., Warneke, T., Notholt, J., Hase, F., Kivi, R., Sussmann, R., Machida, T., Matsueda, H., and Sawa, Y. (2013). Satellite-inferred European carbon sink larger than expected. Atmospheric Chemistry and Physics, 14, 13739–13753. https://doi.org/10.5194/acp-14-13739-2014

[[4]](https://doi.org/10.1175/BAMS-D-15-00310.1) Reuter, M., and Coauthors. (2017). How much CO$_2$ is taken up by the European terrestrial biosphere?. Bullettin of American Meteorology Society, 98, 665–671. https://doi.org/10.1175/BAMS-D-15-00310.1

[[5]](https://acp.copernicus.org/articles/19/14233/2019/) Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A. (2019). Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmospheric Chemistry and Physics, 19, 14233–14251. https://doi.org/10.5194/acp-19-14233-2019

[[6]](https://doi.org/10.5194/acp-19-12067-2019) Wang, H., Jiang, F., Wang, J., Ju, W., and Chen, J. M. (2019). Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals. Atmospheric Chemistry and Physics, 19, 12067–12082. https://doi.org/10.5194/acp-19-12067-2019

[[7]](https://essd.copernicus.org/articles/16/2857/2024/) Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S. (2024). A global surface CO$_2$ flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system. Earth System Science Data, 16, 2857–2876. https://doi.org/10.5194/essd-16-2857-2024

[[8]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes) Copernicus Climate Change Service (C3S). (2024). European State of the Climate 2023, Web site.

[[9]](https://doi.org/10.5194/bg-10-6699-2013) Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X. (2013). Global atmospheric carbon budget: results from an ensemble of atmospheric CO$_2$ inversions. Biogeosciences, 10, 6699–6720. https://doi.org/10.5194/bg-10-6699-2013


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > References
---
ensemble of atmospheric CO$_2$ inversions. Biogeosciences, 10, 6699–6720. https://doi.org/10.5194/bg-10-6699-2013

[[10]](https://acp.copernicus.org/articles/22/9215/2022/) Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M. (2022). Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions. Atmospheric Chemistry and Physics, 22, 9215–9243. https://doi.org/10.5194/acp-22-9215-2022

[[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain) European Centre for Medium-Range Weather Forecasts (ECMWF). Description of the CO2 inversion production chain. Version: 12-December-2025 11:25 (Accessed on 7-January-2026).

[[12]](https://doi.org/10.15138/9N0H-ZH07) Lan, X., Tans, P. and K.W., Thoning. Trends in globally-averaged CO$_2$ determined from NOAA Global Monitoring Laboratory measurements. Version: Monday, 05-May-2025 16:38:58 MDT. https://doi.org/10.15138/9N0H-ZH07

[[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) Copernicus Climate Change Service, Climate Data Store, (2018). Carbon dioxide data from 2002 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.f74805c8 (Accessed on 7-January-2026).

[[14]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160575) European Centre for Medium-Range Weather Forecasts (ECMWF). C3S Greenhouse Gas (GHG). Version: 5-September-2025 10:55 (Accessed on 7-January-2026).

[[15]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2024). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO$_2$ & CH$_4$) data set CDR7 (01.2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.

[[16]](https://acp.copernicus.org/articles/19/14233/2019/) Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A. (2019). Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmospheric Chemistry and Physics, 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019

[[17]](https://doi.org/10.1029/2005JD006390#jgrd12458-bib-0008) Chevallier, F., Fisher, M. , Peylin, P., Serrar, S., Bousquet, P., Bréon, F.-M., Chédin, A., and Ciais P. (2005). Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data. Journal of Geophysical Research, 110, D24309. https://doi.org/10.1029/2005JD006390


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > References
---
and sinks from satellite observations: Method and application to TOVS data. Journal of Geophysical Research, 110, D24309. https://doi.org/10.1029/2005JD006390

[[18]](https://acp.copernicus.org/articles/14/577/2014/)Cressot, C., Chevallier, F., Bousquet, P., Crevoisier, C., Dlugokencky, E. J., Fortems-Cheiney, A., Frankenberg, C., Parker, R., Pison, I., Scheepmaker, R. A., Montzka, S. A., Krummel, P. B., Steele, L. P., and Langenfelds, R. L. (2014). On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements. Atmospheric Chemistry and Physics, 14, 577–592, https://doi.org/10.5194/acp-14-577-2014

[[19]](https://acp.copernicus.org/articles/24/10639/2024/) Wilson, C., Kerridge, B. J., Siddans, R., Moore, D. P., Ventress, L. J., Dowd, E., Feng, W., Chipperfield, M. P., and Remedios, J. J. (2024). Quantifying large methane emissions from the Nord Stream pipeline gas leak of September 2022 using IASI satellite observations and inverse modelling. Atmospheric Chemistry and Physics, 24, 10639–10653, https://doi.org/10.5194/acp-24-10639-2024

[[20]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf) Reuter, M.,Fuentes Andrade, B., Buchwitz, M. (2025). C3S Greenhouse Gas (GHG: CO2 & CH4) v4.6: Algorithm Theoretical Basis Document (ATBD), C3S2_313a_DLR_WP1-DDP-GHG-v1_ATBD_XGHG_v4.6.

[[21]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577) European Centre for Medium-Range Weather Forecasts (ECMWF). C3S Greenhouse Gas (GHG: CO2 & CH4) v4.6: Algorithm Theoretical Basis Document (ATBD). Version: 10-September-2025 8:42 (Accessed on 7-January-2026).

[[22]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160640) European Centre for Medium-Range Weather Forecasts (ECMWF). C3S Greenhouse Gas (GHG: MTCO2 v10.1 & MTCH4 v10.2): Product User Guide and Specification (PUGS). Version: 8-December-2025 10:49 (Accessed on 7-January-2026).

[[23]](https://amt.copernicus.org/articles/13/789/2020/) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications.  Atmospheric Chemistry and Physics, 13, 789–819. https://doi.org/10.5194/amt-13-789-2020, 2020.


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes > ℹ If you want to know more > References
---
2003–2018) for carbon and climate applications.  Atmospheric Chemistry and Physics, 13, 789–819. https://doi.org/10.5194/amt-13-789-2020, 2020.

[[24]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_A_latest.pdf) Boesch, A.,and  Di Noia, A. (2024). Product User Guide and Specification (PUGS) – ANNEX A for products CO2_GOS_OCFP, CH4_GOS_OCFP (v7.3, 2009-2021) & CH4_GOS_OCPR (v9.0, 2009- 2021), C3S2_312a_Lot2_DLR – Atmosphere.

[[25]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_B_latest.pdf) Barr A., and Borsdorff, T. (2024). Product User Guide and Specification (PUGS) – ANNEX B for products CO2_GO2_SRFP, CH4_GO2_SRFP (v2.0.0, 2019-2022), C3S2_312a_Lot2_DLR – Atmosphere.


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring
---
Production date: 03-09-2024

Produced by: Consiglio Nazionale delle Ricerche ([CNR](https://www.cnr.it/en))


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Quality assessment question
---
* **How the variability of spatial and temporal data coverage can affect the quantification of the long-term atmospheric carbon dioxide trends by satellite measurements (XCO2 Level 3 gridded product)?**

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas, representing about 64% of the total radiative forcing by long living greenhouse gases [[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=): in 2022, atmospheric CO2 represented the 150% of the pre-industrial level. In this assessment, atmospheric CO2 spatial seasonal means and trends are analysed using the XCO2 v4.4 Level 3 gridded product (OBS4MIPS)[[2]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf), by adopting an approach similar to [[3]](https://amt.copernicus.org/articles/13/789/2020/).

Code is included for transparency but also learning purposes and gives users the chance to adapt the code used for the assesment as they wish. Since this assessment is an experimental product, users are referred to official sources for accurate reporting (e.g., [[4]](https://climate.copernicus.eu/esotc/2023), [[5]](https://climate.copernicus.eu/global-climate-highlights-2023)).


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Quality assessment statements
---
These are the key outcomes of this assessment

* The dataset "Carbon dioxide data from 2002 to present derived from satellite observations" can be used to evaluate CO2 mean values, climatology and growth rate over the globe, hemispheres or large regions
* Caution should be exercised in certain regions (high latitudes, regions with frequent cloud cover, oceans) where data availability varies along the temporal coverage of the data set: this must be carefully considered when evaluating global or hemispheric information
* For data in high latitude regions or in regions with frequent cloud cover, users should consult uncertainty and quality flags as appropriate for their applications. In addition, for the years 2003-2008 only values over land are available, which has to be taken into account for possible applications of this dataset
```


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Methodology
---
**Spatial seasonal means and trends** are presented and assessed using the new **XCO2 v4.4 Level 3 gridded product (OBS4MIPS)** [[6]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview), which has been generated using the Level 2 EMMA products [[2]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) as input.

**To show how data coverage varies between years and seasons**, we have calculated and plotted the **average XCO2 values for singular seasons and years**.

**Spatial trends** are calculated using a linear model (i.e. Theil-Sen slope estimator) over monthly anomalies (i.e. actual monthly values minus climatological monthly means). **This should be treated with caution as the long-term trend of atmospheric CO2 is not strictly linear**. The statistical significance of the trends is assessed using the Mann-Kendall test. Similar to [[3]](https://amt.copernicus.org/articles/13/789/2020/), only land pixels are considered to avoid artefacts related to different data availability over oceans (the data product is land only for 2003-2008).

'The analysis and results are organised in the following steps, which are detailed in the sections below:'

**[](template:section-1)**
 * Import all the relevant packages.
 * Choose the temporal and spatial coverage, land mask.
 * Cache needed functions.

**[](template:section-2)**
 * In this section, we define the data request to CDS. *Please note that users have to accept the GHG-CCI Licence for getting the dataset (see [[6]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview)).*

**[](template:section-3)**
 * To show how data coverage varies between years and seasons, we have plotted spatial average XCO2 values for seasons and years. Seasons are defined as December - February (DJF), March - May (MAM), June - August (JJA) and September – November  (SON).

**[](template:section-4)**
 * Trends are calculated using a linear model (i.e. Theil-Sen slope estimator) over monthly anomalies (i.e. actual monthly values minus climatological monthly means). The statistical significance of the trends is assessed using the Mann-Kendall test.

**[](template:section-5)**
 * Here main results are summarised

Please note that **no data sub-setting or selection has been performed**, neither as a function of the number of individual Level 2 observations leading to the reported Level 3 values ("co2_nobs"), nor as a function of the standard error ("xco2_stderr") provided in the data files.


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 1. Choose the data to use and set-up the code > Choose temporal and spatial coverage, land mask
---
In this section, we define the parameters to be ingested by the code (users can change the temporal periods of analyses, the region of interest and activate/deactivate the land masking). With the aim to compare our results with existing literature [[7]](https://library.wmo.int/idurl/4/58743), the analysis is limited to time period  from 2012 to 2021. However, specific data subsetting can be set by the users, so that data can be also retrived for other specific periods or regions.

Choose variable (xch4 or xco2)
Choose a time period
Minimum value of land fraction used for masking
min_land_fraction = None  # None: Do not apply mask
Define region for analysis


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 1. Choose the data to use and set-up the code > Chache needed functions
---
In this section, we cached a list of functions used in the analyses.

- The function `get_da` (`get_da_nomask`) is used to subset the data for the defined time period and spatial region by applying (not appliyng) land mask (as a function of  *min_land_fraction*).

- The function `convert_units` rescale xco2 mole fraction to part per millions (ppm).

- The `seasonal_weighted_mean` function extracts the regional means over the selected domains. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon-lat grids used for the reanalysis and for the forecast models. It is used by the function `compute_seasonal_timeseries_nomask` to provide the seasonal xco2 averaged value for each single year (Figure 1).

- The function `compute_anomaly_trends` is used to calculate the trend and the related statistical significance.

- The `compute_monthly_anomalies` function is used to derive the monthly xco2 anomalies before calculating the trends.

Shift years (shift -1 to get D(year-1)J(year)F(year))
Get rid of 1st JF and last D, so it become [MAM, JJA, SON, DJF, ..., SON]
Mann-Kendall
Detrended anomalies

(template:section-2)=


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 2. Retrieve XCO2 data (OBS4MIPS)
---
In this section, we define the data request to CDS (data product OBS4MIPS, Level 3, version 4.4, XCO2).

(template:section-3)=


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 3. Compute and plot the global variability of seasonal XCO2
---
To show how data coverage varies between years and seasons, in this section we plot the spatial average XCO2 values for seasons and years. Seasons are defined as December - February (DJF), March - May (MAM), June - August (JJA) and September – November  (SON).

Analysis of global variability of seasonal XCO2
To invalidate the cache you can pass the argument invalidate_cache=True

```text
100%|██████████| 1/1 [00:00<00:00,  2.95it/s]
```

*Figure 1. Global annual variability of seasonal XCO2. Different panels represent the individual seasons (column) and years (rows). Seasons are defined as December - February (DJF), March - May (MAM), June - August (JJA) and September - November (SON).*

(template:section-4)=


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 4. Compute global and spatial trends
---
In this section, XCO2 spatial trends are calculated and statistical significances is assessed for land pixels (“land_fraction > 0.5”). Global statistics for individual pixels are also reported as ppm/month. Please note that the low growth rates observed in the tropics are probably a result of sparse data availability due to the frequent presence of clouds.

Calculation of global and pixel trends
To invalidate the cache you can pass the argument invalidate_cache=True

```text
100%|██████████| 1/1 [00:00<00:00,  3.43it/s]
```

*Figure 2. Trends of XCO2 given in ppm/month calculated by using linear model (Theil-Sen) and Mann-Kendall test for statistical significance. Over each pixel, trends are calculated for monthly anomalies over land ("land_fraction > 0.5"). The shaded areas indicate pixels that did not pass the Mann-Kendall significance test. Global statistics for individual pixels are shown on the left.*

(template:section-5)=


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > Analysis and results > 5. Results
---
The seasonality of the availability of measurements is evident, with values available in the Southern Hemisphere (SH) mid and high latitudes from September to March, whereas values for the Northern Hemisphere (NH) mid and high latitudes are mostly available from April to August (Figure 1).

For 2012-2021, the mean global XCO2 growth rate is 2.28 ppm/yr (Figure 2). Taking into account the associated uncertainties and the different calculation methods, this value is in good agreement with the value of 2.46 ppm/yr provided by the WMO global in-situ observations [[7]](https://library.wmo.int/idurl/4/58743). The tropics and the middle and high latitudes are the regions that are not covered by measurements throughout the year or are characterised by higher uncertainty (see also [[3]](https://amt.copernicus.org/articles/13/789/2020/)), and therefore the trend values are more variable and in some cases not statistically significant.


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Carbon dioxide data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > ℹ If you want to know more > Example of application
---
Copernicus Climate Change Service (C3S). (2024). [European State of the Climate 2023](https://climate.copernicus.eu/ESOTC/2023), Full report. Specifically, [the section on greenhouse gas concentrations](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-concentrations).


EQC Quality Assessment: "Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring > ℹ If you want to know more > References
---
[[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=) World Meteorological Organization. (2023). WMO Greenhouse Gas Bullettin, 19, ISSN 2078-0796.

[[2]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2023). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO2 & CH4) data set CDR 6 (01.2003-12.2021), C3S project 2021/C3S2_312b_Lot2_DLR/SC1, v6.3.

[[3]](https://amt.copernicus.org/articles/13/789/2020/) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications. Atmospheric Measurement Techniques, 13, 789–819.

[[4]](https://climate.copernicus.eu/esotc/2023) Copernicus Climate Change Service (C3S). (2024). European State of the Climate 2023, Full report.

[[5]](https://climate.copernicus.eu/global-climate-highlights-2023) Copernicus Climate Change Service (C3S). (2024). Global Climate Highlights 2023.

[[6]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) Copernicus Climate Change Service, Climate Data Store, (2018): "Carbon dioxide data from 2002 to present derived from satellite observations". Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.f74805c8.

[[7]](https://library.wmo.int/idurl/4/58743) World Meteorological Organization. (2022). WMO Greenhouse Gas Bullettin, 18, ISSN 2078-0796.


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability
---
Production date: 09-07-2025

Produced by: National Research Council of Italy - Institute of Atmospheric Sciences and Climate, Paolo Cristofanelli and Davide Putero


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Quality assessment question
---
* **Are the XCO$_2$ Level 3 data suitable for identifying regions characterized by high XCO$_2$ values?**
* **What are the uncertainties related to the XCO$_2$ satellite observations?**

Carbon dioxide (CO$_2$) is the most important anthropogenic greenhouse gas, accounting for nearly 64% of the total radiative forcing by long-lived greenhouse gases [[1]](https://library.wmo.int/idurl/4/69057). The concentration of CO$_2$ in the atmosphere in 2023 was 420.0$\,\pm\,$0.1 ppm [[1]](https://library.wmo.int/idurl/4/69057). Monitoring the long-term CO$_2$ variability is crucial for understanding the global carbon sources and sinks, improving [[2]](https://doi.org/10.5194/essd-17-965-2025) our knowledge of the carbon cycle, supporting the development of effective climate policies, and providing more reliable projections for future climate change.

In this assessment, we analyze the spatial variability of atmospheric column-averaged mixing ratios of CO$_2$ (XCO$_2$) over different regions using the Level 3 XCO$_2$ gridded data products (Obs4MIPs, version 4.5 [[3]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)), whose main application is related to the comparison and validation of global climate models (e.g., [[4]](https://doi.org/10.5194/bg-17-6115-2020)).

In particular, by comparison with an independent study based on a gap-free and fine-scale CO$_2$ dataset [[5]](https://doi.org/10.1016/j.envint.2023.108057), we assess the ability of this dataset to reproduce temporal and spatial anomalies in XCO$_2$, also quantifying the uncertainty provided with the dataset.

The code is included for transparency and to allow users to adapt it to their needs.


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Quality assessment statement
---
These are the key outcomes of this assessment

* The XCO$_2$ satellite observations (Level 3, Obs4MIPs, version 4.5) provide a  consistent global description of the spatial XCO$_2$ pattern and can be used to detect areas characterized by elevated XCO$_2$ values. Care should be excercised when considering data from regions affected by high uncertainties. The highest uncertainty values were found over high latitudes, the Himalayas and the tropical rainforest zone due to the occurrence of large solar zenith angles or frequent cloud cover.
* The sampling characteristics of the dataset must be carefully considered when computing annual averages or anomalies, given the low or non-available data over high latitudes, especially during winter months.
* As data availability and uncertainty are not constant over space and time, users are advised to consider the evolution of their values over the spatial regions and time periods of interest.
* Users must be aware that the 5$^\circ$x5$^\circ$ averaging can introduce a smoothing effect in the spatial XCO$_2$ field with respect to the Level 2 datasets.
* The main use of this data product is for comparison with climate models. Users interested in investigating the CO$_2$ fluxes occurring at the Earth's surface are advised to use products specifically designed for this purpose.
```


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Methodology
---
This notebook aims to provide users with information on the availability of XCO$_2$ Level 3 products, both globally and for specific user-defined regions. Additionally, it offers insights into the uncertainties associated with XCO$_2$ measurements and how these variables vary over time.

More specifically, this notebook aims to:
* Produce global maps of yearly XCO$_2$ anomalies for comparison with an external dataset [[5]](https://doi.org/10.1016/j.envint.2023.108057). Please note that, in order to minimise the impact of non-uniform spatial coverage over the different seasons of the Obs4MIPs data product [[3]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf), only data from 60°N to 60°S was considered in global spatial analyses. This approach is similar to that in [[4]](https://doi.org/10.5194/bg-17-6115-2020).
* Generate global maps for the corresponding XCO$_2$ uncertainty ("xco2_stderr").
* Produce maps of XCO$_2$ for specific regions over land [[5]](https://doi.org/10.1016/j.envint.2023.108057).
* Create time series of selected XCO$_2$ variables ("xco2", "xco2_stderr" and "xco2_nobs"), for specific regions. Please note that this analysis can be customized by the user to include one additional variable (not shown here): "xco2_stddev", which represents the standard deviation of the XCO$_2$ Level 2 observations within each grid box.

The analysis and results are organized in the following steps, which are detailed in the sections below:

**[](template:section-1)**
 * Import all relevant packages.
 * Define the temporal and spatial coverage, land mask, and spatial regions for the analysis.
 * Cache needed functions.

**[](template:section-2)**
 * Define the data request to CDS.
 * Define the data request for the external dataset.

**[](template:section-3)**

This section presents several results for the XCO$_2$ products, i.e.:
 * Annual global maps of the XCO$_2$ anomalies for 2015-2020 and comparison between CDS and the external dataset
 * Annual global maps of the XCO$_2$ uncertainties for 2015-2020
 * A focus of the XCO$_2$ maps over specific regions, and considering a single month (January 2016)
 * Global and regional time series of XCO$_2$, related uncertainties and data availability over the entire data period (2003-2022)


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 1. Choose the data to use and set-up the code > Define temporal and spatial coverage, land mask, and spatial regions for the analysis
---
In this section, we define the parameters to be ingested by the code (that can be customized by the user), i.e.:
* the temporal period of analysis;
* the activation/deactivation of the land masking;
* the regions of interest;
* the link to the external dataset.

Single time to display
Range for 2015-2020
External data


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 1. Choose the data to use and set-up the code > Cache needed functions
---
In this section, we cached a list of functions used in the analyses.

* The `convert_units` function rescales XCO$_2$ mole fraction (and related variables, except "xco2_nobs") to parts per million (ppm).

* The `mask_scale_and_regionalise` function extracts the XCO$_2$ data over the selected spatial region. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon/lat grids used for the reanalysis and for the forecast models. It uses the `convert_units` function for rescaling the values to ppm, and it applies the threshold (if any) on the minimum land fraction.

(template:section-2)=


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 2. Retrieve data > 2.1 Obs4MIPs
---
In this section, we define the data request to CDS (data product Obs4MIPs, Level 3, version 4.5, XCO$_2$) and download the datasets (i.e., the 2015-2020 period, and the whole dataset).

Retrieve the 2015-2020 dataset
Retrieve the whole dataset, for plotting the time series

```text
region='global'
```

```text
100%|██████████| 1/1 [00:00<00:00, 27.82it/s]
```

```text
region='global_subset'
```

```text
100%|██████████| 1/1 [00:00<00:00, 12.37it/s]
```

```text
region='north_america'
```

```text
100%|██████████| 1/1 [00:00<00:00, 31.65it/s]
```

```text
region='europe_africa'
```

```text
100%|██████████| 1/1 [00:00<00:00, 11.27it/s]
```

```text
region='east_asia'
```

```text
100%|██████████| 1/1 [00:00<00:00, 43.42it/s]
```

```text
region='global'
```

```text
100%|██████████| 1/1 [00:00<00:00, 34.60it/s]
```

```text
region='global_subset'
```

```text
100%|██████████| 1/1 [00:00<00:00, 38.29it/s]
```

```text
region='north_america'
```

```text
100%|██████████| 1/1 [00:00<00:00,  6.98it/s]
```

```text
region='europe_africa'
```

```text
100%|██████████| 1/1 [00:00<00:00, 23.72it/s]
```

```text
region='east_asia'
```

```text
100%|██████████| 1/1 [00:00<00:00, 44.67it/s]
```


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 2. Retrieve data > 2.2 Zhang et al. (2023) dataset
---
In this section, we define the data request to the external dataset used for the comparison excercise.

```text
region='global'
region='global_subset'
region='north_america'
region='europe_africa'
region='east_asia'
```

(template:section-3)=


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 3. Data analysis for the different variables > Global maps of XCO$_2$ anomalies
---
In this section, global maps of XCO$_2$ anomalies for the years 2015–2020 are presented for the latitudes spanning from 60°N to 60°S. The calculation of anomalies is achieved by the subtraction of the annual global average from the global XCO$_2$ concentrations, in accordance with the approach outlined in [[5]](https://doi.org/10.1016/j.envint.2023.108057). Prior to the execution of the calculations, a land mask was implemented. A comparison was made between the XCO$_2$ anomalies from the Obs4MIPs (version 4.5) dataset and the results from the gap-free and fine-scale XCO$_2$ dataset created by [[5]](https://doi.org/10.1016/j.envint.2023.108057). Prior to the comparison, the external dataset was regridded to a 5° x 5° regular grid by means of an arithmetic unweighted averaging process.

Throughout all the considered years, regions within the 10$^\circ\,$S–40$^\circ\,$N latitude band exhibit positive XCO$_2$ anomalies, consistent with the dataset produced by [[5]](https://doi.org/10.1016/j.envint.2023.108057). Both datasets reported similar features in the interannual variability of XCO$_2$ anomalies, like the higher values over China in 2020 compared to 2019, positive anomalies over Southeast Asia in 2016, and over tropical Africa in 2016, 2017 and 2018.

Focusing on the yearly bias (obtained by subtracting the Zhang et al. [[5]](https://doi.org/10.1016/j.envint.2023.108057) anomalies from the Obs4MIPs anomalies), the absolute differences in the yearly anomalies were mostly related to the high latitudes and the tropical regions. Please note that the horizontal coverage of the XCO2_OBS4MIPS dataset (version 4.5) extends approximately from 60$^\circ\,$S to 70$^\circ\,$N [[6]](https://amt.copernicus.org/articles/13/789/2020/). Even though the XCO2_OBS4MIPS dataset was subsetted in this exercise to exclude high latitudes (i.e., higher than 60°N and 60°S), comparisons with the gap-free external dataset by Zhang et al. [[5]](https://doi.org/10.1016/j.envint.2023.108057) should be treated with extreme caution at latitudes above 50°N. This is due to non-uniform data coverage across different seasons for Obs4MIPs (version 4.5) at high latitudes, which can lead to sampling bias when data is aggregated on a yearly timescale. During the investigated period, the external dataset [[5]](https://doi.org/10.1016/j.envint.2023.108057) provided XCO$_2$ also for Greenland, a region over which Obs4MIPs (version 4.5) did not provide data. This is due to the high surface albedo, which hinders the reliability of satellite retrievals, even during periods of illumination.

Moreover, the dataset [[5]](https://doi.org/10.1016/j.envint.2023.108057) appears to provide a smoother spatial field of XCO$_2$ anomalies with respect to Obs4MIPs (version 4.5), probably due to the use of a learning machine algorithm to produce this gap filled dataset.

Unisci tutti e 3 i set
Stack delle dimensioni (year, product) in "panel"
Assegna etichette leggibili ai pannelli

*The figure shows the multi-year global distribution of XCO$_2$ anomalies (from 2015 to 2020) derived from the XCO2_OBS4MIPS dataset (version 4.5) as well as for the gap-filled dataset produced by Zhang et al. (2023) and the bias (Obs4MIPs - Zhang et al.) between the two datasets. Please be aware that the data displayed here includes only locations with latitudes below 60°N and 60°S.*


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 3. Data analysis for the different variables > Global maps of XCO$_2$ uncertainties
---
In this section, we show the multi-year global maps of the XCO$_2$ reported uncertainties, i.e., the "xco2_stderr" variable. This parameter is defined as the standard error of the average including single sounding noise and potential seasonal and regional biases [[3]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf). The yearly averages are calculated by averaging the monthly values provided by the considered dataset. Throughout all the considered years some features emerge, with higher uncertainties over high latitudes, the Himalayas, and the tropical rainforest zone, which are the regions where we observed larger deviations between Obs4MIPs (version 4.5) and the external dataset [[5]](https://doi.org/10.1016/j.envint.2023.108057). This is mainly due to the sparseness in sampling because of frequent cloud cover, as well as large solar zenith angles in high latitudes, which is a challenge for accurate XCO$_2$ retrievals [[6]](https://amt.copernicus.org/articles/13/789/2020/).

*The figure shows the multi-year global distribution of XCO$_2$ standard error (from 2015 to 2020), derived from the XCO2_OBS4MIPS dataset (version 4.5).*


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 3. Data analysis for the different variables > Regional maps of XCO$_2$
---
In this section, we show the maps of the XCO$_2$ values for January 2016, both globally and over specific regions. The selected regions (i.e., North America, Europe/Africa, and East Asia) were chosen according to [[5]](https://doi.org/10.1016/j.envint.2023.108057). A direct comparison for January 2015 was not possible, as the Level 3 XCO$_2$ data are missing for that month in the XCO2_OBS4MIPS dataset (version 4.5). The XCO$_2$ patterns are similar and comparable, especially for Europe/Africa and East Asia. However, the Level 3 data cover lesser areas than the product in [[5]](https://doi.org/10.1016/j.envint.2023.108057), with missing data especially at latitudes above 50$^\circ\,$N.

To select specific regions
fig.suptitle(f"{variable =} {time = }")

*The figure shows the spatial distribution of XCO$_2$ values over the globe and selected regions, derived from the XCO2_OBS4MIPS dataset (version 4.5), for January 2016.*


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > Analysis and results > 3. Data analysis for the different variables > Time series analysis for the different variables
---
In this section, we show the time series for two XCO$_2$ variables (i.e., "xco2" and "xco2_stderr"), considering both the entire dataset and specific regions of interest. For each variable, each plot displays the monthly spatial average for each region, along with the corresponding monthly standard deviation.
This analysis would provide the user with:
* the time series of XCO$_2$ values ("xco2") for each region, highlighting the positive trends;
* the regional change over time of the uncertainties associated to the XCO$_2$ values ("xco2_stderr").
* the time series of "xco2_nobs", which denotes the number of individual XCO$_2$ Level 2 observations used to compute the monthly Level 3 data.

In particular, an increase in the uncertainties ("xco2_stderr") is observed after 2009, likely due to the increase in the number of algorithms used to calculate the median XCO$_2$ data [[7]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). A further increase in the uncertainties is observed from 2020 onwards, and this is probably linked to both the increase in the number of algorithms and the change in the minimum number of algorithms required to calculate the median XCO$_2$ [[7]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). In addition, regions with more difficult retrieval conditions due to frequent cloud cover (e.g., East Asia) show larger uncertainties due to larger inter-algorithm spreads [[6]](https://amt.copernicus.org/articles/13/789/2020/).

The temporal variability of "xco2_nobs" traces the changes over time of the input data availability, and the number of used algorithms to obtain the merged Level 2 data products (XCO2_EMMA), from which the Obs4MIPs data product is derived (please note that the standard deviations have not been reported for "xco2_nobs" in order to increase the plot's readability.).
The first time period (up to April 2009) is characterised by a significant number of soundings by the SCIAMACHY sensor. The subsequent period (2009-September 2014) reflects the reduced number of single soundings provided by SCIAMACHY with the contribution from GOSAT. The following period, which extends up to 2019, reflects the contributions from both GOSAT and OCO-2. In contrast, the final period of the dataset reflects the contributions from GOSAT, OCO-2, and GOSAT-2. A comprehensive analysis of the number of soundings contained within the EMMA database on a monthly basis, along with the comparative contribution of each individual Level 2 algorithm to the EMMA database, can be found in [[7]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf).

To select specific regions
If variaable is xco2_nobs, use log scale for y-axis
ax.set_ylabel("n observations")
Imposta tick logaritmici automatici

*The figure shows the monthly time series of XCO$_2$ (top panels) and their associated uncertainties (middle panels) for the entire dataset, including global and regional analyses. The blue lines represent the monthly spatial average, while the shaded areas indicate $\pm$1 standard deviation. The bottom panels shows the time series of monthly spatial average of the  number of individual XCO$_2$ Level 2 observations used to compute Obs4MIPs (Level 3) data. The main titles indicate the variable shown, while the subtitles specify the corresponding regions.*


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Carbon dioxide data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)

Users interested in the investigation of CO$_2$ fluxes can consider to use specifically designed Copernicus resources like [CAMS global inversion-optimised greenhouse gas fluxes and concentrations](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview).


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > ℹ If you want to know more > References
---
[[1]](https://library.wmo.int/idurl/4/69057) World Meteorological Organization. (2024). WMO Greenhouse Gas Bulletin, 20, ISSN 2078-0796.

[[2]](https://doi.org/10.5194/essd-17-965-2025) Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Landschützer, P., Le Quéré, C., Li, H., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Arneth, A., Arora, V., Bates, N. R., Becker, M., Bellouin, N., Berghoff, C. F., Bittig, H. C., Bopp, L., Cadule, P., Campbell, K., Chamberlain, M. A., Chandra, N., Chevallier, F., Chini, L. P., Colligan, T., Decayeux, J., Djeutchouang, L. M., Dou, X., Duran Rojas, C., Enyo, K., Evans, W., Fay, A. R., Feely, R. A., Ford, D. J., Foster, A., Gasser, T., Gehlen, M., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A. K., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Kato, E., Keeling, R. F., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Lan, X., Lauvset, S. K., Lefèvre, N., Liu, Z., Liu, J., Ma, L., Maksyutov, S., Marland, G., Mayot, N., McGuire, P. C., Metzl, N., Monacci, N. M., Morgan, E. J., Nakaoka, S.-I., Neill, C., Niwa, Y., Nützel, T., Olivier, L., Ono, T., Palmer, P. I., Pierrot, D., Qin, Z., Resplandy, L., Roobaert, A., Rosan, T. M., Rödenbeck, C., Schwinger, J., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Steinhoff, T., Sun, Q., Sutton, A. J., Séférian, R., Takao, S., Tatebe, H., Tian, H., Tilbrook, B., Torres, O., Tourigny, E., Tsujino, H., Tubiello, F., van der Werf, G., Wanninkhof, R., Wang, X., Yang, D., Yang, X., Yu, Z., Yuan, W., Yue, X., Zaehle, S., Zeng, N., and Zeng, J. (2025). Global Carbon Budget 2024, Earth System Science Data, 17, 965-1039.

[[3]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2024). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO$_2$ & CH$_4$) data set CDR7 (01.2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.


EQC Quality Assessment: "Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: uncertainty-quality-flags_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability > ℹ If you want to know more > References
---
) data set CDR7 (01.2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.

[[4]](https://doi.org/10.5194/bg-17-6115-2020) Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., and Eyring, V. (2020). Spatially resolved evaluation of Earth system models with satellite column-averaged CO$_2$, Biogeosciences, 17, 6115–6144.

[[5]](https://doi.org/10.1016/j.envint.2023.108057) Zhang, L., Li, T., Wu, J., and Yang, H. (2023). Global estimates of gap-free and fine-scale CO$_2$ concentrations during 2014-2020 from satellite and reanalysis data, Environment International, 178, 108057.

[[6]](https://amt.copernicus.org/articles/13/789/2020/) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications, Atmospheric Measurement Techniques, 13, 789–819.

[[7]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf) Reuter, M., and Buchwitz, M. (2024). Algorithm Theoretical Basis Document (ATBD) – ANNEX D for products XCO2_EMMA, XCH4_EMMA, XCO2_OBS4MIPS, XCH4_OBS4MIPS (v4.5, CDR7, 2003-2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.1b.


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring
---
Production date: 21-11-2025

Produced by: Davide Putero (CNR), Paolo Cristofanelli (CNR)


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Quality assessment questions
---
* **How can the variability of spatial and temporal data coverage affect the quantification of the long-term atmospheric methane trends by satellite measurements (XCH$_4$ Level 3 gridded product)?**
* **What are the uncertainties related to the XCH$_4$ satellite observations?**

Methane (CH$_4$) is the second most important anthropogenic greenhouse gas after carbon dioxide (CO$_2$), representing about 19% of the total radiative forcing by long-lived greenhouse gases [[1]](https://library.wmo.int/idurl/4/68532). Atmospheric CH$_4$ also adversely affects human health as a precursor of tropospheric ozone [[2]](https://doi.org/10.1073/pnas.0600201103). Monitoring the long-term CH$_4$ variability is therefore crucial for monitoring the emission reductions [[3]](https://doi.org/10.5194/essd-17-1873-2025). In this assessment, atmospheric CH$_4$ spatial seasonal means and trends are analysed using the XCH$_4$ Level 3 gridded product (Obs4MIPs, version 4.5 [[4]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)), by adopting an approach similar to [[5]](https://doi.org/10.5194/amt-13-789-2020). Furthermore, we also evaluate the uncertainty associated with this dataset.

The Obs4MIPs XCH$_4$ product is generated by spatial (5°x5°) and temporal (monthly) gridding of the corresponding EMMA Level 2 data product. For more details see [[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). By analysing an ensemble of different Level 2 datasets from various retrieval algorithms, the EMMA algorithm generates a dataset containing  XCH$_4$ from individual retrievals. In particular, for each month and 10°×10° grid box, the algorithm with the grid box mean closest to the median is selected. The list of the considered retrieval algorithms is available from [[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). The various retrieval algorithms are optimised for different instruments (SCIAMACHY, GOSAT, GOSAT-2) which measure backscattered solar radiation in the near-infrared for O$_2$ as well as the absorption bands of CO$_2$ or CH$_4$.

Code is included for transparency but also learning purposes and gives users the chance to adapt the code used for the assesment as they wish. Users should always check product documentation and associated peer-reviewed papers for a more complete reporting of the issues discussed here (e.g., [[7]](https://doi.org/10.24381/14j9-s541), [[8]](https://climate.copernicus.eu/global-climate-highlights-2024)).


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Quality assessment statements
---
These are the key outcomes of this assessment

* The dataset "Methane data from 2002 to present derived from satellite observations" can be used to evaluate CH$_4$ mean values, climatology and growth rate over the globe, hemispheres or large regions.
* Caution should be exercised in certain regions (high latitudes, regions with frequent cloud cover, oceans) where data availability varies along the temporal coverage of the dataset: this must be carefully considered when evaluating global or hemispheric information.
* For data in high latitude regions or in regions with frequent cloud cover, users should consult uncertainty and quality flags as appropriate for their applications. In addition, for period November 2005 - March 2009 only values over land are available, which has to be taken into account for possible applications of this dataset.
* As the uncertainties associated with the dataset are not constant over space and time, users are advised to consider the evolution of their values over the spatial regions and time periods of interest. The highest uncertainty values were found over high latitudes, the Himalayas and the tropical rainforest zone.
```


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Methodology
---
Spatial seasonal means and trends are presented and assessed using the XCH$_4$ v4.5 Level 3 gridded product (Obs4MIPs), which has been generated using the Level 2 EMMA products [[4]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) as input.

To show how data coverage varies between years and seasons, we have calculated and plotted the average XCH$_4$ values for the different seasons and years, over the period 2013-2022. This time period has been chosen for the purpose of comparing the calculated trends with the results in [[1]](https://library.wmo.int/idurl/4/68532).

Spatial trends are calculated using a linear model (i.e., Theil-Sen slope estimator) over monthly anomalies (i.e., actual monthly values minus climatological monthly means). This should be treated with caution as the long-term trend of atmospheric CH$_4$ is not strictly linear. The statistical significance of the trends is assessed using the Mann-Kendall test. Similar to [[5]](https://doi.org/10.5194/amt-13-789-2020), only land pixels are considered to avoid artefact related to different data availability over oceans (for period November 2005 - March 2009 only values over land are available).

The global maps of the product uncertainties ("xch4_stderr") for a specific subset of years (2015-2020) are shown, together with the time series of selected XCH$_4$ variables ("xch4", "xch4_stderr" and "xch4_nobs"). Please note that this analysis can be extended to one additional variable, not shown here ("xch4_stddev"), or can be customized by the user for specific regions of interest.

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](template:section-1)**
 * Import all the relevant packages.
 * Choose the temporal and spatial coverage, land mask, and possible spatial regions for the analysis.
 * Cache needed functions.

**[](template:section-2)**
 * In this section, we define the data request to CDS.

**[](template:section-3)**
 * To show how data coverage varies between years and seasons, we have plotted spatial average XCH$_4$ values for seasons and years. To avoid the production of too many maps, this analysis has been limited to the period 2013-2022. Seasons are defined as December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON).

**[](template:section-4)**
 * Trends (over 2013-2022) are calculated using a linear model (i.e., Theil-Sen slope estimator) over monthly anomalies (i.e., actual monthly values minus climatological monthly means). The statistical significance of the trends is assessed using the Mann-Kendall test.

**[](template:section-5)**
 * Annual global maps of the XCH$_4$ uncertainties are presented, for a subset of data (2015-2020).
 * Global time series of XCH$_4$, related uncertainties and data availability over the entire data period (2003-2022) are shown.


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 1. Choose the data to use and set-up the code > Choose temporal and spatial coverage, land mask
---
In this section, we define the parameters to be ingested by the code (that can be customized by the user), i.e.:
* the temporal period of analysis;
* the activation/deactivation of the land masking;
* the regions selected for the analysis. Please note that, for this notebook, only the global maps and time series are reported.

The analyses presented in this notebook cover different time periods:
* with the aim to compare our results with existing literature [[1]](https://library.wmo.int/idurl/4/68532), the global variability and trend analyses are limited to 2013-2022;
* the global plots of the XCH$_4$ uncertainties refer to the years from 2015 to 2020;
* the time series of XCH$_4$, related uncertainties and data availability cover the entire period of data coverage.

Choose variable
Choose a time period (to be used for the global variability and trend analysis)
Minimum value of land fraction used for masking
Define regions for analysis


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 1. Choose the data to use and set-up the code > Chache needed functions
---
In this section, we cached a list of functions used in the analyses.

- The function `get_da` (`get_da_nomask`) is used to subset the data for the defined time period and spatial region by applying (not appliyng) the land mask (as a function of *min_land_fraction*).

- The function `convert_units` rescales XCH$_4$ mole fraction to parts per billion (ppb).

- The `seasonal_weighted_mean` function extracts the regional means over the selected domains. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon/lat grids used for the reanalysis and for the forecast models. It is used by the function `compute_seasonal_timeseries_nomask` to provide the seasonal XCH$_4$ average value for each year (Fig. 1).

- The function `compute_anomaly_trends` is used to calculate the trend and the related statistical significance.

- The `compute_monthly_anomalies` function is used to derive the monthly XCH$_4$ anomalies before calculating the trends.

- The `mask_scale_and_regionalise` function extracts the XCH$_4$ data over the selected spatial region. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon/lat grids used for the reanalysis and for the forecast models. It uses the `convert_units` function for rescaling the values to ppb, and it applies the threshold (if any) on the minimum land fraction.

Shift years (shift -1 to get D(year-1)J(year)F(year))
Get rid of 1st JF and last D, so it become [MAM, JJA, SON, DJF, ..., SON]
Mann-Kendall
Detrended anomalies

(template:section-2)=


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 2. Retrieve XCH$_4$ data (Obs4MIPs)
---
In this section, we define the data request to CDS (data product Obs4MIPs, Level 3, version 4.5, XCH$_4$) and download the dataset.

```text
region='global'
```

```text
0%|          | 0/1 [00:00<?, ?it/s]/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/cacholote/extra_encoders.py:297: SerializationWarning: cannot serialize global coordinates {'time_bnds', 'lat_bnds', 'pre_bnds', 'lon_bnds'} because the global attribute 'coordinates' already exists. This may prevent faithful roundtrippingof xarray datasets
  obj.to_netcdf(tmpfilename)
100%|██████████| 1/1 [00:00<00:00,  1.45it/s]
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/scipy/stats/_mstats_basic.py:1256: RuntimeWarning: All `x` coordinates are identical.
  return stats_theilslopes(y, x, alpha=alpha, method=method)
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
```

(template:section-3)=