Skip to main content

METHODS article

Front. Remote Sens., 30 September 2021
Sec. Satellite Missions
Volume 2 - 2021 |

The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions

www.frontiersin.orgGianpaolo Balsamo1* www.frontiersin.orgRichard Engelen1 www.frontiersin.orgDaniel Thiemert1 www.frontiersin.orgAnna Agusti-Panareda1 www.frontiersin.orgNicolas Bousserez1 www.frontiersin.orgGrégoire Broquet2 www.frontiersin.orgDominik Brunner3 www.frontiersin.orgMichael Buchwitz4 www.frontiersin.orgFrédéric Chevallier2 www.frontiersin.orgMargarita Choulga1 www.frontiersin.orgHugo Denier Van Der Gon5 www.frontiersin.orgLiesbeth Florentie6 www.frontiersin.orgJean-Matthieu Haussaire2 www.frontiersin.orgGreet Janssens-Maenhout7 www.frontiersin.orgMatthew W. Jones8 www.frontiersin.orgThomas Kaminski9 www.frontiersin.orgMaarten Krol10 www.frontiersin.orgCorinne Le Quéré8 www.frontiersin.orgJulia Marshall11 www.frontiersin.orgJoe McNorton1 www.frontiersin.orgPascal Prunet12 www.frontiersin.orgMaximilian Reuter4 www.frontiersin.orgWouter Peters10 www.frontiersin.orgMarko Scholze13
  • 1European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, United Kingdom
  • 2French Alternative Energies and Atomic Energy Commission, CEA, Paris, France
  • 3Swiss Federal Laboratories for Materials Science and Technology, EMPA, Dübendorf, Switzerland
  • 4Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 5Department of Climate, Air and Sustainability, TNO, Utrecht, Netherlands
  • 6Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  • 7European Commission, Joint Research Centre, JRC, Ispra, Italy
  • 8University of East Anglia, UEA, Norwich, United Kingdom
  • 9Inversion Lab, ILab, Hamburg, Germany
  • 10Wageningen University and Research, WUR, Wageningen, Netherlands
  • 11Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre Oberpfaffenhofen, Germany
  • 12SPASCIA, Ramonville-Saint-Agne, France
  • 13Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M).


The CO2 Human Emissions project (CHE, has responded to the task set by the European Commission to coordinate and support the development of a European capacity to monitor anthropogenic carbon dioxide (CO2) emissions. Designed as a Coordination and Support Action, CHE has advanced on the building blocks of a CO2 Monitoring and Verification Support (CO2MVS) for the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC). This historical binding pact signed by 196 nations in 2015, aims at limiting greenhouse gas emissions through Nationally Determined Contributions and a 5-yearly Global Stocktakes process that sit in an Enhanced Transparency Framework. To support this process the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations (EOs) to mainly target anthropogenic CO2 emissions. This is a major observational, technological, infrastructural and scientific challenge. The monitoring of fossil fuel CO2 emissions must come with a reported uncertainty estimate that can be useful for policymakers (e.g., for targeting actions to lower uncertainties in hotspots of interest). In this context, the main approaches to estimate fossil fuel emissions, apart from the inventories, are based on the Earth observations and modelling. Inverse transport models, either used on their own or within a coupled carbon cycle fossil fuel data assimilation system, provide these so called “top-down” emission estimates. These can be driven by observations of not only CO2 but also co-emitted species such as nitrogen oxides (NOx) and of variables that constrain the emission processes such as nightlight intensity, used to locate human activity related to anthropogenic CO2 emissions, or Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), used to constrain the biogenic CO2 naturally absorbed by vegetation during the photosynthesis.

The setting up of a CO2MVS capacity follows an ambitious multi-year roadmap (Janssens-Maenhout et al., 2020), addressing current limitations in observation availability for both insitu and satellite observations (Ciais et al., 2014), as well as indicating the need for significantly improving our modelling and data assimilation capabilities. The development of those components has been specifically targeted to enhance our capacity to separate anthropogenic CO2 emissions from natural CO2 variability. The CO2MVS, once operational, will be a key asset to quantify the effectiveness of policy-driven changes, supporting the European ambition of reaching Climate-neutrality by 2050, proposing a European Climate Law and a European Climate Pact (Delbeke and Vis, 2019).

Scientific studies of the carbon cycle tend to fall into two categories: “bottom-up” and “top-down”.

In “bottom-up” emissions approaches, using process models and inventories, spatially heterogeneous information based on our knowledge of emission sources and their evolution over time can be combined. Bottom-up emission incorporate our knowledge of biological processes that drive the exchange of CO2 between the atmosphere and the land and ocean. Inventory-based emissions tend to be more accurate for country-scale annual-mean estimates, especially for countries that have detailed procedures in place, but when these estimates are extrapolated to include much higher spatial and temporal resolution, uncertainties increase. Models based on our knowledge of biogeochemical processes, still have significant uncertainties, however improving those processes has not been a primary focus of the CHE project.

In “top-down” or “inverse” techniques, measurements of CO2 abundance in space and time are used to infer the large-scale uptake and release of CO2 at the surface. However, owing to the coarse spatial resolution adopted (coarser than 50 km), present-day inversion systems have clear difficulties in disentangling sources and sinks at local scales, and even bigger challenges in separating fossil fuel and other human-induced emissions from natural fluxes.

A synergetic solution is found through the combination of “top-down” and “bottom-up” approaches, as applied for instance in the framework of climate reanalysis (e.g., Hersbach et al., 2020) or biogeochemistry (Rayner et al., 2019). Further requirements are identified in the enhancement of resolution and transport accuracy (Agusti-Panareda et al., 2019) and by merging the available knowledge from emission inventories and process models with the increasing amount of observational data for the atmosphere and the Earth’s surface. The Fossil Fuel Data Assimilation System (FFDAS; Asefi-Najafabady et al., 2014; Super et al., 2020a) and the Carbon Cycle Data Assimilation System (CCDAS; Rayner et al., 2005; Scholze et al., 2019) approaches represent significant efforts to bridge “top-down” and “bottom-up” approaches.

A mature and credible monitoring system for anthropogenic CO2 emissions should be able to integrate all available information streams from Earth observations, inventories and activity data, and models of the atmosphere, land and ocean, which is a complex undertaking.

The CHE project started in October 2017, bringing together a consortium of 22 European partners and lasting for over 3 years. By the end of the CHE project, the global-scale developments have 1) demonstrated the high-resolution CO2 modelling capability in global Nature-runs (Agusti-Panareda et al., 2021), 2) integrated updated global CO2 sectoral emission datasets (Choulga et al., 2020), 3) constructed a new high-resolution (∼10 km) input dataset for fossil CO2 emissions since the 1960s (Jones et al., 2021), and 4) advanced the use of Ensemble-based uncertainty characterisation preparing the data assimilation step (McNorton et al., 2020). Sizeable advances at European scale include the realisation of high-resolution CO2 emission inventories (Super et al., 2020b) that served also as exploratory studies of what can be achievable at global scale, where high quality inventories are made available. Similarly, the global modelling and data assimilation advances (Bousserez, 2019; Barré et al., 2020) had beneficial links and interactions, comparing the methodological work done over Europe and exploiting a wealth of dataset gathered within the VERIFY partner project (

CHE also supported some rapid response studies during the 2020 COVID-19 pandemic, estimating a 17% decrease in global daily CO2 emissions during the initial outbreak phase (Le Quéré et al., 2020; Liu et al., 2020), which has stimulated advances in the use of human activity data for rapid and continuous assessment of CO2 emissions (e.g., These results have been supported by EO-based estimations (Buchwitz et al., 2020; Chevallier et al., 2020; Weir et al., 2020; Zheng et al., 2020).

The CHE Horizon 2020 project ran from October 2017 to December 2020. As a Coordination and Support Action, CHE actively brought together European expertise to introduce a consolidated approach of building an operational anthropogenic CO2 emission monitoring and verification support capacity. There were four main areas of work, covering:

1) Observations,

2) Emission inventories,

3) Modelling and

4) Inversion systems.

The three central questions that CHE addressed are:

• What does it take to have a combined “bottom-up” and “top-down” estimation system capable of distinguishing the anthropogenic part of the CO2 budget from the natural fluxes?

• How can we make the first steps towards such a system that can use the high spatial and temporal resolution of satellite observations to monitor anthropogenic emissions at the required time scales?

• What does it take to transform a research system into a fully operational monitoring support capacity?

This paper summarises some of the key achievements towards the development of a CO2MVS prototype, as well as the definition of an implementation plan which includes requirements and priorities in consideration of the calendar described within the Paris Agreement and in the European Commission CO2 Task Force reports (Ciais et al., 2016; Pinty et al., 2017; Pinty et al., 2019; the CO2 blue, red, and green reports respectively, The CHE developments and findings have been transferred to a new project, which will develop a prototype Copernicus CO2 Service (CoCO2 project, and will run from January 2021 to December 2023. This follow-on project has a particular focus on supporting the first Global Stocktake of the Paris Agreement to be held in 2023. It will have a particular focus on the implementation and readiness of both the monitoring prototype and the information product portfolio that can support an operational phase. This will be done in close coordination with the European Commission, nations that are party to the United Nations Framework Convention on Climate Change, and international stakeholders.

In the following sections a description of the methodology developed, and selected results, are presented to provide a synthesis of the key CHE achievements in this first phase of the CO2MVS development, which continues within the Copernicus CO2 Prototype project and are embedded in the evolution of the Copernicus Atmosphere Monitoring and Climate Change Services (CAMS and C3S).


In 2015, a first report from the European Commission CO2 Task Force, Ciais et al. (2016), proposed a European support capacity for monitoring anthropogenic CO2 emissions and concluded that a comprehensive observing system should be based on a combination of space-borne observations and ground-based monitoring networks.

Inverse transport modelling (Bergamaschi et al., 2018) still relies on the availability of prior fossil fuel CO2 emission estimates and uncertainties, as well as prior biogenic fluxes and uncertainties, and provides posterior fossil fuel CO2 emission estimates. However, inversions often do not integrate the full process knowledge and often neglect atmospheric transport uncertainties, which Schuh et al. (2019) have highlighted as a major source of bias in annual carbon budgets.

The global system used in CHE rely on the ECMWF Integrated Forecast System and the experience gained within the Copernicus Atmospheric Monitoring System Re-Analysis—CAMSRA (Inness et al., 2019). The capacity to assimilate a large amount of remote sensing data informative of atmospheric concentrations and optimally combined with atmospheric composition and transport modelling, is a clear advantage of the integrated approach developed in CHE, which extend this capability for generating a posterior fossil fuel CO2 emission estimate, consistently integrating both Earth observations and process knowledge accounting for the uncertainties in each of the building blocks.

Building Blocks: Observations, Modelling, Assimilation, Uncertainty

The requirements for integrating Earth observation in an Earth System Model via data assimilation methodology, in the context of a CO₂ monitoring service, should account for the multiscale aspect of the problem (Figure 1). Multiscale in this context refers to both the spatial and temporal domains represented in a prototype system.


FIGURE 1. Temporal and spatial scales of CO2 monitoring from plumes to global CO2 growth rate.

For the spatial domain a challenge of detection is inherently linked with the local nature of anthropogenic emissions as they emanate from stacks, cars, and buildings (point sources, <100 m scale). The resulting CO₂ in the atmosphere travels over hundreds of kilometres while interacting strongly with natural ecosystems (from 1 to 100 km), weather systems (from 10 to 1,000 km) and eventually across the full hemisphere (>10,000 km) and the rest of the globe. Not one modelling system can capture all these scales, and strengths of global scale models thus need to be combined with other modelling approaches (e.g., Regional Models, Lagrangian, Gaussian plume, Large-eddy simulations).

In the temporal domain it is recognized that signals of anthropogenic emissions are stronger and easier to detect close to their source but get diluted at the typical boundary layer mixing time scale of 15–30 min (Broquet et al., 2018; Kuhlmann et al., 2019).

The key requirements within the CHE project stem from research done in the work packages dedicated to scientific advances and from connecting the specific requirements of the CHE Monitoring and Verification System prototype. These are detailed in the CHE deliverables Chevallier (2020, D5.2), Agusti-Panareda and Brunner (2020, D5.4), Peters and Krol (2020, D5.6), Scholze et al. (2020a, D.5.8), respectively, covering the Earth observations, the modelling components, the data assimilation methodology and the uncertainty characterisation.

The global monitoring system must allow us to separate the impact of anthropogenic emissions from the effect of the complex natural carbon cycle, while observation requirements may not yet be fulfilled (Ciais et al., 2014) for both the anthropogenic and biogenic components, since both emissions simultaneously affect atmospheric CO2 concentrations.

Although observations from satellites, ground-based observation networks and aircraft provide CO2 information at specific times and locations, alone they do not constitute a continental to global monitoring capacity across different time scales. Moreover, these observations mostly measure atmospheric CO2 abundances at a given location, which is not directly informative of the underlying carbon emissions or uptake. Therefore, the use of atmospheric transport models or an Earth System modelling infrastructure is required to combine Earth observations (ground-based, aircraft and satellite) with detailed CO2 emissions inventory data.

The impacts of the CHE project are all linked to its function as a bridge between the European Commission and its CO2 Task Force, space agencies and related industries, the CO2 science community, and the Copernicus Services. The capacity building aspects of CHE focused on strengthening the links between these sectors and using these to scope the required architecture of a future CO2 emission monitoring system.

It is important to note that the CHE project’s impact is not directly related to end users. The impact results from providing building blocks that will make possible future operational services, which will then serve several categories of end users. The future end-users can be found in the policy sector, the science community and the private sector, as outlined in the European Commission’s CO2 report. However, liaising with final end-users is also required in designing a system that can meet the needs by 2025 and 2030. The VERIFY project has already dedicated efforts towards end-user products and achieved two important syntheses for CO2, and methane (CH4) and nitrogen oxides (NOx) (Petrescu et al., 2020a; Petrescu et al., 2020b; Petrescu et al., 2020). This work will continue in the CoCO2 project from 2021 on, with a handover of a new VERIFY synthesis in 2022, targeting 2021 emissions. Existing international efforts, such as the annual synthesis provided by the Global Carbon Project (e.g., Friedlingstein et al., 2020) provide extremely valuable science-consensus datasets complementary to the finalities of the CO2MVS that aims at monitoring applications.

Design Considerations on Scales, Species, Streams

The CHE prototype will encompass multiple scales, species and streams in order to support the global, regional and local information. The approaches consist of:

• Multi-scale approach to monitor emissions from point sources (power stations or industrial facilities), cities and countries using different model domains from global, regional to local and model resolutions (e.g., from 25 km to 100 m).

• Multi-species approach to detect and attribute the observed atmospheric signal to specific sources/sinks (e.g., natural and anthropogenic emissions with sectorial distribution).

• Multi-stream approach to support different applications and users with a near-real time stream focusing on shorter synoptic timescales designed to provide early warnings and giving feedback to data producers, and a re-analysis stream that uses consolidated quality-controlled data, products and models with their associated uncertainties to estimate trends.

Capacity Building and Developments

Global Monitoring and Verification Support Capacity

The CHE prototype for the global MVS capacity aims at providing global integrated CO2 emissions and concentrations at a resolution sufficiently high to enable the representation of large emissions and their evolution in the atmosphere. The availability of reliable CO2 concentrations and their transport will provide lateral boundary conditions for regional-scale and local scale inversions. Moreover, the availability of ensemble-based CO2 realisations (McNorton et al., 2020) will enable offline modelling and coupled assimilation efforts to refine emissions detection capabilities, adapted to the CO2 long-lived atmospheric concentration (Bousserez, 2019). In parallel to the CO2 developments, exploratory studies for the CH4 (Barré et al., 2020) have shown the capability of the CAMS system for local emission detection.

The ECMWF Integrated Forecasting System (IFS) that supports the Copernicus Atmosphere Monitoring Service (CAMS GHG forecasts) is currently running globally at 9 km (high-resolution (HRES) configuration, a single realisation). The operational ensemble weather forecast suite runs at 18 km (ENS) initialised by an ensemble data assimilation (EDA) configuration, with 50 members (Buizza et al., 1999; Leutbecher et al., 2017).

The new High Performance Computing infrastructure will permit exploration of a combination of the ENS/EDA/HRES at around 9 km foreseen in 2023. Moreover, new initiatives supported by the European Destination Earth Initiative ( further explore the impact of horizontal and vertical resolution and of more sophisticated biogeochemistry, with the aim to attain a resolution of 4 km by 2025 and 1 km by 2030, thanks to advanced supercomputing infrastructure and software innovations aiming at building a digital twin of planet Earth (Wedi et al., 2020).

The global MVS will provide 1) a robust, reliable, timely system to support the Global Stocktake with monthly estimates of EO-driven CO2 emissions and their uncertainties, and 2) a Regional MVS capacity (see below and Table 1), both well nested in a development plan that benefits from synergies with the other Copernicus services.


TABLE 1. Global and Regional Modelling Systems participating to the CO2 prototype phase (from the H2020 CHE and CoCO2 projects).

Hotspot Monitoring and Verification Support Capacity

Hotspot or point scale inversions will permit the monitoring of emissions at local scale, for those locations where observation availability enables the sampling of plumes. Point scale simulations will benefit from global and regional scales for the provision of boundary conditions and prior information. In return, the local scale knowledge can support the error characterisation for both the regional and global scale MVS, as they will need CO2 emissions inventories as prior estimates. The question of model error characterisation was addressed using Large-Eddy-Simulations detailed in Klonecki and Prunet, (2020, CHE D2.8). The stochastic dynamics of the plume under turbulent conditions leads to spatio-temporal variability in concentrations of CO2 emitted from point sources. This variability, which should be taken as a source of uncertainty for inversions based on episodic measurements from polar-orbiting satellites, was quantified at scales typical of CO2 space-based measurements. Preliminary evaluation provided in Klonecki and Prunet, (2020, CHE D2.8) suggests significant turbulent-induced variability on XCO2 at the scale of satellite measurements (of the order of 20%), with possible biases on flux retrieval if not properly taken into account. Work is foreseen to use Large-Eddy-Simulations for deriving a reliable model representation uncertainty for local scale transport of power plant plumes. Turbulent features not captured by the forward modelling used in the inversion/assimilation process can be parametrised using the Large-Eddy-Simulations dataset.

The study of well-known emission hotspots has demonstrated the synergy of satellite observations of CO2 and NO2 (Reuter et al., 2019) in the case of isolated sources. Assessing interannual variability of CO2 emissions remains a challenge with the current satellite coverage (Buchwitz et al., 2020; Chevallier et al., 2020; Weir et al., 2020; Zheng et al., 2020). However, city-scale monitoring capability has great potential with the increased data quality and availability offered by the CO2 satellite mission (Meijer et al., 2020) as demonstrated for the city of Berlin by Kuhlmann et al. (2019, 2020). While the CO2 monitoring capacity brought by CO2 Monitoring mission will be a key asset of the future CO2MVS (e.g., greater accuracy of detection, larger swath, higher spatial resolution and constellation of satellites), presence of clouds and large amounts of aerosol-load still pose challenges to the observational coverage in some areas. These caveats, related to observability, will result in higher uncertainties. Expanding ground-based observing networks should therefore be among the actionable responses, in agreement with recommendations by Chevallier et al. (2020). There are however also caveats in representation of processes that need sufficient resolution and precision (Agusti-Panareda et al., 2019) to be more directly comparable to the CO2 Monitoring mission observations.

A data assimilation system that would just target one of the scales involved in natural and anthropogenic CO2 variability would not capture the integrated emissions over larger areas. It would thus require continuous observations almost everywhere, which is not feasible even with new (satellite) instruments and techniques. The integral of CO₂ emissions and uptake is moreover a very useful constraint to quantify changes in biospheric uptake and release over ecosystem/country scales, needed to understand the annual carbon balance. A system that can combine scales from minutes up to weeks/months would thus represent the best of both worlds. Hereafter the key advances in each of the building blocks are discussed.

Earth Observations Developments

CO2 observations of fluxes and concentrations with other types of Earth observation data such as radiocarbon, NO2, oxygen, solar-induced fluorescence and carbonyl sulphide are reviewed in Chevallier (2020, CHE D5.2). These are clustered in satellite CO2 and non-CO2, ground-based remote sensing, in situ and flask-sampling observations. The relevant information from the Copernicus CO2 Monitoring Mission Requirements Document and from the three reports of the Copernicus Expert group and of the CO2 Task Force is included. Research needs for the identification of the role of each relevant Earth observation type in the Copernicus CO2 support capacity system are identified, for data streams currently available. The synthetic satellite data instead aim at supporting studies for CO2 Monitoring mission satellite constellation and are detailed in Strandgren, (2020, CHE D2.5). From experience gained within CHE and CAMS, the NO2 and Solar Induced Fluorescence (SIF) satellite-based data are identified as global-coverage Earth observation information with currently more direct usability for data assimilation purposes. In situ observing capability is paramount to Evaluation and Quality Control of the CO2MVS. Sizeable advances in the Earth observations capability covering both satellite-based remote sensing (e.g., Copernicus Sentinel-5P, NASA OCO-3) and the ground-based network (e.g., the TCCON—Total Carbon Column Observing Network, the FLUXNET micrometeorological sites and the ICOS—Integrated Carbon Observation System sites) are documented in (Ciais et al., 2014).

Modelling Developments

The modelling and prior components, subdivided in atmospheric transport from both resolved transport (advection schemes) and unresolved sub-grid processes (convection and turbulence), biogenic fluxes and anthropogenic emissions, are reviewed inAgusti-Panareda and Brunner (2020, CHE D5.4). The high-resolution regional nature runs, nested in the European runs (described in Haussaire et al. (2020), CHE D2.4), are themselves nested in the global Tier-1 runs performed with the ECMWF/CHE-CAMS system (described in Agusti-Panareda (2019), CHE D2.2). These simulations are produced using two separate models, COSMO-GHG and LOTOS-EUROS. COSMO-GHG is used for both the meteorology and tagged tracers of multiple anthropogenic and biogenic sources. The meteorological outputs drive the offline model LOTOS-EUROS, which computes reactive trace gases and aerosols on top of the tagged tracers. A comparison of the different transport models and prior datasets is included in Agusti-Panareda and Brunner (2020, CHE D5.4) to assess the different capabilities of the models and priors used to perform the CHE library of simulations. The Tier-2 global nature runs (Agusti-Panareda et al., 2021), see Figure 2, constitute a step improvement with respect to the Tier-1 runs, in both atmospheric transport and surface emissions, and demonstrate the incremental improvement cycle that will support the CO2MVS capacity. The lessons learnt on the key CO2 modelling aspects span from the importance of high spatial resolution and accurate atmospheric transport (Agusti-Panareda et al., 2019) to the importance of specifying a vertical profile for emission sources not released at the surface level (Brunner et al., 2019), as in the case for industries stacks. An example of the European regional simulation is provided in Figure 3 to illustrate the differences across systems, that reflect difference in spatial resolution, handling of processes, and choice of CO2 inventory dataset.


FIGURE 2. Examples of the CHE Nature Run Tier-2 CO2 concentrations valid for the December 12, 2015 obtained by global simulations with the ECMWF-IFS. Video for the whole 2015 available at


FIGURE 3. Examples of CHE European regional CO2 mole fractions valid for the February 17, 2015, centred over Berlin (Germany) with a focus on stacks from industry and power plants obtained by simulation with WRF-GHG, COSMO-GHG and CHIMERE (see Table 1), and comparison with the ECMWF-IFS.

Data Assimilation Developments

The data assimilation methodologies for CO2 distinguish in online 4D-Var, offline 4D-Var, online EnKF, offline EnKF, offline analytical and hybrid ensemble Var varieties, which are reviewed in Peters and Krol (2020, CHE D5.6). The differences between direct flux estimation (transport inversion) and the inclusion of models for fossil fuel emissions (FFDAS) and biospheric fluxes (CCDAS) are discussed with their implications on the control vector configuration, and the error covariances statistics, along with examples of existing inversion systems. A configuration for global and regional inversions is presented. This includes a multi-scale and multi-species data assimilation system that targets anthropogenic CO₂ emissions and is capable of ingesting multiple streams of observations, including satellite observations.

A hybrid 4D-Var ensemble approach (Bousserez, 2019) implemented in an online transport model, and operated within the Numerical Weather Prediction environment (Bonavita et al., 2016), was identified as a fundamental building block towards extending the Data Assimilation system capability to using constraints from multiple tracers and long 4D-Var windows for joint atmospheric state and surface fluxes optimisation. This methodology accommodates operational constraints (e.g., computational efficiency, seamless integration into current Data Assimilation system) by combining existing ECMWF products, such as the adjoint-based 4D-Var algorithm (Courtier et al., 1994) and ensemble simulations (Buizza et al., 1999; Leutbecher et al., 2017).

Additionally, a novel approach to integrate multi-scale and multi-model posterior emission products (i.e., regional, local inversions) into the global IFS prototype has been proposed that consists of directly assimilating those external outputs as observations. Such integration effort would help improve the flow of information across different CO2 inversion products and equally applied to CO and CH4. This will facilitate interpretation of the data assimilation results and enhance usefulness for users and stakeholders by providing a unified framework for Carbon inversions. A preliminary short-window 4D-Var prototype has been developed within the CHE project, building on previous CO2 data assimilation implementation in the IFS (Engelen et al., 2009; Massart et al., 2016; Massart et al., 2020) and aided by recent infrastructure for an augmented control variable. Figure 4 shows the geographical distribution from a CO emission inversion using the new prototype. The CO emission scaling factors show sizable corrections over Asia, Africa, and South America, while smaller localised corrections can be seen over Europe, reflecting the better knowledge in the prior emission inventories over those developed countries. Looking at vertical profiles of CO concentration in Figure 5 significant improvements are obtained by the 12-h analysis compared to the modelled CO concentrations prior, evaluated with independent aircraft profile observations, both in the lower and upper troposphere.


FIGURE 4. (A) Daily unitless scaling factors for CO emission on 29-05-2019 based on augmented state 4D-Var assimilation of MOPITT and IASI CO observations; (B) same as a) but on 30-05-2019.


FIGURE 5. Relative difference (model-observation) in CO concentration between IFS analysis and aircraft measurements over Atlanta (A) and Mumbai (B) for a reference state-only (blue) and a joint atmosphere/surface-emission analysed state (red).

Uncertainty Characterisation Developments

The components in the sub-sections above are all characterised by uncertainties in space and time that need to be realistically represented and that are detailed in Scholze et al. (2020b, CHE D5.8). The posterior uncertainty is evaluated in Observing System Simulated Experiments and in the Quantitative Network Design studies, within the CHIMERE and CCFFDAS systems, respectively. The CCFFDAS allowed to assess several design aspects of the upcoming MVS capacity as part of the Copernicus CO2 Monitoring mission.

The assessment was based on the Quantitative Network Design technique (Kaminski and Rayner, 2017) and quantified the mission’s performance in terms of the posterior uncertainty in the total CO2 emissions classified into two sectors, one for electricity generation and the other for all other emissions denoted as the “other” sector. Analysis of two different observing networks, ground based in situ observations and satellite based total column observations, in a range of configurations is detailed in Scholze et al. (2020a, CHE D3.6).

We also have numerically assessed how anthropogenic CO2 emissions are depending on country of origin based on IPCC 2006 Guidelines and its Refinement of 2019 (Choulga et al., 2020), see Figure 6. These uncertainties gridded globally at 36 and 9 km resolutions, provided prior uncertainty information for CO2 ensemble runs (McNorton et al., 2020), see Figure 7, and Tier-2 nature runs (Agusti-Panareda et al., 2021), see Figure 2, respectively.


FIGURE 6. Upper bound of global annual anthropogenic CO2 emission uncertainty, in % (logarithmic scale) (A), and illustration of European sectoral emissions with uncertainties bounds (b) from Choulga et al. (2020). In the example of sectoral emissions Europe 27 + United Kingdom is shown (B), with Energy production subdivided in super-emitters S and average emitters A, and four main emission sectors are represented for Manufacturing, Settlement, Aviation and Transport, with all remaining emission sources clustered in “Other”.


FIGURE 7. Total column CO2 global model error from a 50-member IFS ensemble (A) accounting for uncertainties in meteorological initial conditions, model physics, anthropogenic emissions and the biogenic feedback after a 10-days simulation in January 2015, and (B) illustration of the CO2 ensemble spread at Caltech, United States compared with Total Carbon Column Observing Network (TCCON) observations with grey thin lines represents each of the single ensemble member simulations, from McNorton et al. (2020).

The atmospheric uncertainty in CO2, shown in Figure 7, is the combined effect of anthropogenic emission uncertainties (largest over emission hotspots in eastern China, and smaller signals over North America, Europe and the Middle East), as well as biogenic emission uncertainties in areas with high net ecosystem exchange, such as the Amazon and Southern Africa.

While a full representation of biogenic related uncertainties (e.g., structural vegetation properties, land-use-change) is not yet developed, this study has highlighted the importance of accounting for flow dependent errors and the interplay of biogenic and anthropogenic CO2 emissions.

Also, as part of the CHE project, Jones et al. (2021) have produced a gridded global dataset of CO2 emissions and their uncertainty at 0.1-degree resolution to be used as a prior for multi-decadal runs of “top-down” models (e.g., Friedlingstein et al., 2020), where uncertainties respect the country and sector of origin, relevant for the target of consistent climate reanalysis (Dee et al., 2014).

Implementation Priorities

Among the CHE significant achievements there are the preparation steps needed for the first global stocktake of the Paris Agreement, which have been documented with identified priorities along with the prototype system design as briefly detailed in the two following sub-sections.

Support the 2021 Global Stocktake in 2023

This set of recommendations focuses on the follow-on work to CHE, that will be taken up in the CoCO2 project:

• A “step-wise” approach to the MVS prototype will be followed according to the priorities defined in Chevallier (2020, CHE D5.2), Agusti-Panareda and Brunner (2020, CHE D5.4), Peters and Krol (2020, CHE D5.6), Scholze et al. (2020b, CHE D5.8) to achieve a prototype by 2023.

• Three scales: global, regional, and hotspots will have different setups and observation/modelling possibilities. The global scale system will serve the regional/local scale by providing boundary conditions. Regional/local systems will serve as important benchmarks for the global CHE prototype.

• The modelling resolutions of the first CHE prototype will focus on the highest resolution of 9 km globally with support from an ensemble system to characterise uncertainties. An exploratory 4 km system will be tested.

• An Near Real Time/early-warning system (for satellite monitoring, and attribution studies) with all available Near Real Time observations (L2 and/or radiances) supported by a multi-scale data assimilation approach will aim at ensuring consistency across scales.

• A delay-mode reanalysis (with focus on the Global Stocktake) with best quality observations ancillary/inventories will be developed by 2023 and applied to 2021.

• CO2MVS demonstrations and case studies will benefit from application to CO2, CO, and CH4.

• The Benchmarking with observations that are not used in the assimilation steps will be essential for Evaluation and Quality Control.

• Cross-comparison between systems will provide a way forward to gain further insights on the prototype.

Proposed Prototype Configurations

The proposed configurations to cover the domain and stream are reported in Table 2. The CO2MVS service structure, outline in Figure 8, will operate in interaction with the relevant agencies, as outlined in Figure 9. More details are provided in the Supplementary Material.


TABLE 2. Proposed CHE prototype configurations for global, regional and local domains.


FIGURE 8. Schematic of the anthropogenic CO2 emissions Monitoring and Verification Support capacity (CO2MVS) as developed in the CHE project and adopted by the CO2 Monitoring Task Force. The foreseen service provision elements are depicted by the coloured boxes, while the required continuous development of the operational services is depicted by the white boxes.


FIGURE 9. High-level schematic of the envisaged interaction between the Copernicus CO2MVS capacity and countries as part of the UNFCCC reporting process.


This paper summarises and discusses the CHE project advances, linking CO2 service elements with the scientific work and outlining the preoperational setup that will be further developed in the CoCO2 project 2021–2023.

CHE has advanced on the development of a European CO2 monitoring and verification support capacity for anthropogenic CO2 emissions: enhancing the global, regional and local CO2 simulation capabilities, with focus on anthropogenic source representation, moving towards a global CO2 inversion capability at high resolution to connect atmospheric concentrations to surface emissions, and demonstrating the use of Earth observations (satellite and ground), as well as proxy human activity data, to constrain uncertainties and to enhance the CO2 monitoring timeliness, and to continuously evaluate its quality.

The timeliness of the monitoring suite will especially depend on the availability of the input satellite data (e.g., current commitment from ESA and EUMETSAT for the CO2 Monitoring mission is 24 h after sensing), but also on more detailed user requirements. The exact schedule for a reanalysis inclusive of the CO2 discussed here, will also have to be defined and tested based on user requirements and reprocessing capabilities by EUMETSAT. Furthermore, all service provision activities at ECMWF will have to be linked to and coordinated with the contracted service provision activities as well as with other relevant activities within CAMS (e.g., NO2). All these aspects are a critical element of the ramp-up phase and will require significant time and resources. ECMWF has gained expertise with the implementation and operation of the current C3S and CAMS services, which will benefit the introduction of the CO2MVS in an operational environment. A track record of successfully converting science into operational services is key to engage European expertise implementing a CO2 Copernicus service element.

The CoCO2 project will continue and expand the CHE developments with particular focus on supporting the first Global Stocktake of the Paris Agreement and advancing the implementation and readiness of both the monitoring prototype and the information products portfolio that can support an operational phase, in close coordination with the European Commission, the United Nations, and National and International stakeholders.

Team List

The CHE Team include all co-authors plus the following CHE project contributors: Yvan Baillion (TAS), Jérôme Barré (ECMWF), Gerard Blankenstijn (SRON), Hartmut Boesch (ULEIC), Guillaume Bonnery (ADS SAS), Heinrich Bovensmann (UB), François-Marie Bréon (CEA-LSCE), Laure Brooker (ADS SAS), Ben Brown (ECMWF), Andre Butz (UHEI), Hans Chen (ULUND), Stephanie Christian (DLR), Philippe Ciais (CEA-LSCE), Monica Crippa (JRC), Stijn Dellaert (TNO), Antoine Dussaux (TAS), Gregory Duveiller (JRC), Alie Feikema (KNMI), Heidi Fjeldstad (NILU), Vincent Gabaglio (EUMETSAT), Giulia Galluccio (CMCC), Irma Habermann (ULUND), Maximilian Harlander (ADS GmbH), Piet Helfrich (KNMI), Dick Heslinga (TNO), Sander Houweling (SRON/VU), Michael Jähn (EMPA), Martin Jung (MPG), Ute Karstens (ICOS-CP at ULUND), Gerbert Kets (WU), Andrzej Klonecki (SPASCIA), Jasper Krauser (ADS GmbH), Annet Krijgsman (KNMI), Jeroen Kuenen (TNO), Thomas Lauvaux (CEA-LSCE), Anton Leemhuis (TNO), Rasmus Lindstrot (EUMETSAT), Francisco Lopes (JRC), Astrid Manders (TNO), Andrew Manning (UEA), Sandrine Mathieu (TAS), Guillaume Monteil (ULUND), Rosemary Munro (EUMETSAT), Jacob A. Nelson (MPI-BGC), Tonatiuh Ramirez Nuñez (MPG), Dario Papale (CMCC), Mark Parrington (ECMWF), Penelope Pickers (UEA), Ignacio Pisso (NILU), Elise Potier (CEA-LSCE), Rani Randhawa (ECMWF), Isabelle Rault (CEA-LSCE), Christian Retscher (EUMETSAT), Friedemann Reum (SRON), Corinne Sacher (MPG), Arjo Segers (TNO), Alessanda Settimi (CMCC), Denis Simeoni (TAS), Johan Strandgren (DLR), Ingrid Super (TNO), Patrick Talavera (ADS SAS), Nikola Tatalovic (EMPA), Rona Thompson (NILU), Ronald van der A (KNMI), Anne-Marie van Nes (TNO), Michiel van Weele (KNMI), Michael Voßbeck (iLab), Sophia Walther (MPI-BGC), Alex Webb (ULEIC), Katharina Witt (MPG).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

GB prepared the manuscript with contributions from all co-authors. The CHE team is acknowledged for the comments to the draft manuscript and for the development contributions.


The CHE project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant agreement No 776186. The CoCO2 project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant agreement No 958927.

Conflict of Interest

PP was employed by SPASCIA

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The authors wish to thank Erik Andersson and Bernard Pinty (EC-DEFIS), Mark Dowell (EC-JRC), Nils Wedi and Massimo Bonavita (ECMWF), Xubin Zeng (GEWEX Co-chair), Paolo Ruti and Ruediger Land (EUMETSAT) and Yasjka Meijer (ESA CO2M) for their fruitful comments. The CHE Advisory board chairs Han Dolman (VU Amsterdam) and Peter Rayner (U. Melbourne) together with Miss Monika Kacic (REA) and Prof. Stephen Briggs (U. Cambridge/U. Reading) are specially thanked for their stewardship assured all along the CHE project duration. Finally, CHE work would not have seen the light without the inspirational support of the ECMWF directorate. The CHE and CoCO2 projects have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N. 776186 for CHE, and N. 958927 for CoCO2, respectively.

Supplementary Material

The Supplementary Material for this article can be found online at:


Agusti-Panareda, A., and Brunner, D. (2020). Progress Report on Service Elements for CO2 Emission and Transport Models Integration. CHE D5.4 report. Available at:

Google Scholar

Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., et al. (2019). Modelling CO2 Weather - Why Horizontal Resolution Matters. Atmos. Chem. Phys. 19, 7347–7376. doi:10.5194/acp-19-7347-2019

CrossRef Full Text | Google Scholar

Agusti-Panareda, A., McNorton, J., Balsamo, G., Baier, B., Bousserez, N., Boussetta, S., et al. (2021). Global Nature Runs Data Provide Realistic High-Resolution Carbon-Weather for the Year of the Paris Agreement. Nature Scientific Dataset. (in preparation).

Agusti-Panareda, A. (2019). The CHE Tier1 Global Nature Run. CHE D2.2 report. Available at:

Google Scholar

Asefi-Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., et al. (2014). A Multiyear, Global Gridded Fossil Fuel CO2emission Data Product: Evaluation and Analysis of Results. J. Geophys. Res. Atmos. 119 (10), 10213–10231. doi:10.1002/2013JD021296

CrossRef Full Text | Google Scholar

Balsamo, G., Agusti-Panareda, A., Albergel, C., Arduini, G., Beljaars, A., Bidlot, J., et al. (2018). Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. Remote Sensing 10, 2038. doi:10.3390/rs10122038

CrossRef Full Text | Google Scholar

Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., et al. (2020). Systematic Detection of Local CH4 Emissions Anomalies Combining Satellite Measurements and High-Resolution Forecasts. Atmos. Chem. Phys. Discuss. 21, 5117–5136. doi:10.5194/acp-21-5117-2021

CrossRef Full Text | Google Scholar

Bergamaschi, P., Danila, A. M., Weiss, R., Ciais, P., Thompson, R. L., Brunner, D., et al. (2018). Atmospheric Monitoring and Inverse Modelling for Verification of Greenhouse Gas Inventories. EUR - Scientific and Technical Research Reports, Publications Office of the European Union. doi:10.2760/759928

CrossRef Full Text

Bonavita, M., Hólm, E., Isaksen, L., and Fisher, M. (2016). The Evolution of the ECMWF Hybrid Data Assimilation System. Q.J.R. Meteorol. Soc. 142, 287–303. doi:10.1002/qj.2652

CrossRef Full Text | Google Scholar

Bousserez, N. (2019). Towards a Prototype Global CO2 Emissions Monitoring System for Copernicus. Available at:

Google Scholar

Boussetta, S., Balsamo, G., Agusti-Panareda, A., Arduini, G., Beljaars, A., Dutra, E., et al. (2021). ECLand: an ECMWF Land Surface Modelling Platform. MDPI Atmosphere. 12, 723. doi:10.3390/atmos12060723 (in preparation).

CrossRef Full Text

Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M., Bovensmann, H., et al. (2018). The Potential of Satellite Spectro-Imagery for Monitoring CO2 Emissions from Large Cities. Atmos. Meas. Tech. 11, 681–708. doi:10.5194/amt-11-681-2018

CrossRef Full Text | Google Scholar

Buchwitz, M., Reuter, M., Noël, S., Bramstedt, K., Schneising, O., Hilker, M., et al. (2020). Can a Regional-Scale Reduction of Atmospheric CO2 during the COVID-19 Pandemic Be Detected from Space? A Case Study for East China Using Satellite XCO2 Retrievals. Atmos. Meas. Tech. Discuss. 14, 2141–2166. doi:10.5194/amt-14-2141-2021

CrossRef Full Text | Google Scholar

Buizza, R., Milleer, M., and Palmer, T. N. (1999). Stochastic Representation of Model Uncertainties in the ECMWF Ensemble Prediction System. Q.J.R. Meteorol. Soc. 125, 2887–2908. doi:10.1002/qj.49712556006

CrossRef Full Text | Google Scholar

Chevallier, F. (2020). Progress Report on Service Elements for CO2 Earth Observation Integration CHE D5.2 Report. Available at:

Google Scholar

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. Atmos. Chem. Phys. 19, 14233–14251. doi:10.5194/acp-19-14233-2019

CrossRef Full Text | Google Scholar

Chevallier, F., Zheng, B., Broquet, G., Ciais, P., Liu, Z., Davis, S. J., et al. (2020). Local Anomalies in the Column‐Averaged Dry Air Mole Fractions of Carbon Dioxide across the Globe during the First Months of the Coronavirus Recession. Geophys. Res. Lett. 47, e2020GL090244. doi:10.1029/2020GL090244

CrossRef Full Text | Google Scholar

Choulga, M., Janssens-Maenhout, G., Super, I., Agusti-Panareda, A., Balsamo, G., Bousserez, N., et al. (2020). Global Anthropogenic CO2 Emissions and Uncertainties as Prior for Earth System Modelling and Data Assimilation. Earth Syst. Sci. Data Discuss. doi:10.5194/essd-2020-68

CrossRef Full Text | Google Scholar

Ciais, P., Crisp, D., Denier van der Gon, H., Engelen, R., Janssens-Maenhout, G., Heimann, M., et al. (2016). Towards a European Operational Observing System to Monitor Fossil CO2 Emissions: Final Report from the Expert Group. Luxemberg: European Commission –. 978-92-79-43482-9. doi:10.2788/350433

CrossRef Full Text

Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P. J., et al. (2014). Current Systematic Carbon-Cycle Observations and the Need for Implementing a Policy-Relevant Carbon Observing System. Biogeosciences 11, 3547–3602. doi:10.5194/bg-11-3547-2014

CrossRef Full Text | Google Scholar

Courtier, P., Thépaut, J.-N., and Hollingsworth, A. (1994). A Strategy for Operational Implementation of 4D-Var, Using an Incremental Approach. Q.J R. Met. Soc. 120, 1367–1387. doi:10.1002/qj.49712051912

CrossRef Full Text | Google Scholar

De Rosnay, P., Drusch, M., Vasiljevic, D., Balsamo, G., Albergel, C., and Isaksen, L. (2013). A Simplified Extended Kalman Filter for the Global Operational Soil Moisture Analysis at ECMWF. Q.J.R. Meteorol. Soc. 139, 1199–1213. doi:10.1002/qj.2023

CrossRef Full Text | Google Scholar

De Rosnay, P., Muñoz-Sabater, J., Albergel, C., Isaksen, L., English, S., Drusch, M., et al. (2020). SMOS Brightness Temperature Forward Modelling and Long Term Monitoring at ECMWF. Remote Sensing Environ. 237, 111424. doi:10.1016/j.rse.2019.111424

CrossRef Full Text | Google Scholar

Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., and Thépaut, J.-N. (2014). Toward a Consistent Reanalysis of the Climate System. Bull. Amer. Meteorol. Soc. 95 (8), 1235–1248. doi:10.1175/BAMS-D-13-00043.1

CrossRef Full Text | Google Scholar

Delbeke, J., and Vis, P. (2019). Towards a Climate Neutral Europe: Curbing the Trend. Available at:

Google Scholar

Engelen, R. J., Serrar, S., and Chevallier, F. (2009). Four-dimensional Data Assimilation of Atmospheric CO2using AIRS Observations. J. Geophys. Res. 114, D03303. doi:10.1029/2008JD010739

CrossRef Full Text | Google Scholar

Engelen, R. (2020b). Report on Workshops Organised by CHE, CHE D6.4 Report. Available at:

Google Scholar

Engelen, R. (2020a). Strategic Research Agenda, CHE D6.3 Report. Available at:

Google Scholar

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen, A., et al. (2020). Global Carbon Budget 2020. Earth Syst. Sci. Data 12, 3269–3340. doi:10.5194/essd-12-3269-2020

CrossRef Full Text | Google Scholar

Haussaire, J.-M., Brunner, D., and Segers, A. (2020). Regional Nature Runs. CHE D2.4 (Part I) Report. Available at:

Google Scholar

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. Q.J.R. Meteorol. Soc. 146, 1999–2049. doi:10.1002/qj.3803

CrossRef Full Text | Google Scholar

Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., et al. (2019). The CAMS Reanalysis of Atmospheric Composition. Atmos. Chem. Phys. 19, 3515–3556. doi:10.5194/acp-19-3515-2019

CrossRef Full Text | Google Scholar

Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., et al. (2020). Towards an Operational Anthropogenic CO2 Emissions Monitoring and Verification Support Capacity, BAMS, BAMS-D-19-0017 101(8), E1439–E1451. doi:10.1175/BAMS-D-19-0017.1

CrossRef Full Text | Google Scholar

Jones, M. W., Andrew, R. M., Peters, G. P., Janssens-Maenhout, G., De-Gol, A. J., Ciais, P., et al. (2021). Gridded Fossil CO2 Emissions and Related O2 Combustion Consistent with National Inventories 1959-2018. Sci. Data 8, 2. doi:10.1038/s41597-020-00779-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaminski, T., Knorr, W., Schürmann, G., Scholze, M., Rayner, P. J., Zaehle, S., et al. (2013). The BETHY/JSBACH Carbon Cycle Data Assimilation System: Experiences and Challenges. J. Geophys. Res. Biogeosci. 118, 1414–1426. doi:10.1002/jgrg.20118

CrossRef Full Text | Google Scholar

Kaminski, T., and Rayner, P. J. (2017). Reviews and Syntheses: Guiding the Evolution of the Observing System for the Carbon Cycle through Quantitative Network Design. Biogeosciences 14, 4755–4766. doi:10.5194/bg-14-4755-2017

CrossRef Full Text | Google Scholar

Klonecki, A., and Prunet, P. (2020). LES Simulations Report, CHE D2.8 Report. Available at:

Google Scholar

Kuhlmann, G., Broquet, G., Marshall, J., Clément, V., Löscher, A., Meijer, Y., et al. (2019). Detectability of CO2 Emission Plumes of Cities and Power Plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission. Atmos. Meas. Tech. 12, 6695–6719. doi:10.5194/amt-12-6695-2019

CrossRef Full Text | Google Scholar

Kuhlmann, G., Brunner, D., Broquet, G., and Meijer, Y. (2020). Quantifying CO2 Emissions of a City with the Copernicus Anthropogenic CO2 Monitoring Satellite mission. Atmos. Meas. Tech. 13, 6733–6754. doi:10.5194/amt-13-6733-2020

CrossRef Full Text | Google Scholar

Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J. P., Abernethy, S., Andrew, R. M., et al. (2020). Temporary Reduction in Daily Global CO2 Emissions during the COVID-19 Forced Confinement. Nat. Clim. Chang. 10, 647–653. doi:10.1038/s41558-020-0797-x

CrossRef Full Text | Google Scholar

Leutbecher, M., Lock, S. J., Ollinaho, P., Lang, S. T. K., Balsamo, G., Bechtold, P., et al. (2017). Stochastic Representations of Model Uncertainties at ECMWF: State of the Art and Future Vision. Q.J.R. Meteorol. Soc. 143, 2315–2339. doi:10.1002/qj.3094

CrossRef Full Text | Google Scholar

Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., et al. (2020). Near-real-time Monitoring of Global CO2 Emissions Reveals the Effects of the COVID-19 Pandemic. Nat. Commun. 11, 5172. doi:10.1038/s41467-020-18922-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., et al. (2016). Ability of the 4-D-Var Analysis of the Gosat Besd Xco2 Retrievals to Characterize Atmospheric Co2 at Large and Synoptic Scales. Atmos. Chem. Phys. 16, 1653–1671. doi:10.5194/acp-16-1653-2016

CrossRef Full Text | Google Scholar

Massart, S., Bormann, N., Bonavita, M., and Lupu, C. (2020). Skin Temperature Analysis for the Assimilation of Clear-Sky Satellite Radiances. ECMWF Tech. Memoranda, 870. doi:10.21957/goe0ads8z

CrossRef Full Text | Google Scholar

McNorton, J. R., Bousserez, N., Agustí-Panareda, A., Balsamo, G., Choulga, M., Dawson, A., et al. (2020). Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46r1. Geosci. Model. Dev. 13, 2297–2313. doi:10.5194/gmd-13-2297-2020

CrossRef Full Text | Google Scholar

Meijer, Y., Boesch, H., Bombelli, A., Brunner, D., Buchwitz, M., Ciais, P., et al. (2020). Copernicus CO2 Monitoring Mission Requirements Document, ESA, Reference EOP-SM/3088/YM-ym. available at: (Issue 3.0 10 01, 2020).84.

Google Scholar

Peters, W., and Krol, M. (2020). Progress Report on Service Elements for Data Assimilation Methodology. CHE D5.6 report. available at:

Google Scholar

Petrescu, A. M. R., McGrath, M. J., Andrew, R. M., Peylin, P., Peters, G. P., Ciais, P., et al. (2020a). The Consolidated European Synthesis of CO2 Emissions and Removals for EU27 and UK: 1990-2018. Earth Syst. Sci. Data Discuss., 13, 2363–2406. doi:10.5194/essd-13-2363-2021

CrossRef Full Text | Google Scholar

Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R. L., Peylin, P., McGrath, M. J., et al. (2020b). The Consolidated European Synthesis of CH4 and N2O Emissions for EU27 and UK: 1990-2018. Earth Syst. Sci. Data Discuss, 13, 2307–2362. doi:10.5194/essd-13-2307-2021

CrossRef Full Text | Google Scholar

Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S., et al. (2016). A New Stepwise Carbon Cycle Data Assimilation System Using Multiple Data Streams to Constrain the Simulated Land Surface Carbon Cycle. Geosci. Model. Dev. 9, 3321–3346. doi:10.5194/gmd-9-3321-2016

CrossRef Full Text | Google Scholar

Pinty, B., Ciais, P., Dee, D., Dolman, H., Dowell, M., Engelen, R., et al. (2019). An Operational Anthropogenic CO2 Emissions Monitoring & Verification Support Capacity – Needs and High-Level Requirements for In Situ Measurements: Report from the CO₂ Monitoring Task Force, EUR 29817 EN. Eur. Comm. Jt. Res. Centre, 1–72. doi:10.5194/essd-13-2307-2021

CrossRef Full Text | Google Scholar

Pinty, B., Janssens-Maenhout, G., Dowell, M., Zunker, H., Brunhes, T., Ciais, P., et al. (2017). An Operational Anthropogenic CO2 Emissions Monitoring & Verification System – Baseline Requirements, Model Components and Functional Architecture, EUR 28736 EN. Eur. Comm. Jt. Res. Centre. 13, 1–92. doi:10.2760/08644

CrossRef Full Text | Google Scholar

Rayner, P. J., Michalak, A. M., and Chevallier, F. (2019). Fundamentals of Data Assimilation Applied to Biogeochemistry. Atmos. Chem. Phys. 19, 13911–13932. doi:10.5194/acp-19-13911-2019

CrossRef Full Text | Google Scholar

Rayner, P. J., Scholze, M., Knorr, W., Kaminski, T., Giering, R., and Widmann, H. (2005). Two Decades of Terrestrial Carbon Fluxes from a Carbon Cycle Data Assimilation System (CCDAS). Glob. Biogeochem. Cycles 19, a–n. doi:10.1029/2004GB002254

CrossRef Full Text | Google Scholar

Reuter, M., Buchwitz, M., Schneising, O., Krautwurst, S., O'Dell, C. W., Richter, A., et al. (2019). Towards Monitoring Localized CO2 Emissions from Space: Co-located Regional CO2 and NO2 Enhancements Observed by the OCO-2 and S5P Satellites. Atmos. Chem. Phys. 19, 9371–9383. doi:10.5194/acp-19-9371-2019

CrossRef Full Text | Google Scholar

Scholze, M., Broquet, G., Chen, H., Choulga, M., Kaminski, T., McNorton, J., et al. (2020b). Final Report on Service Element Requirements for Uncertainty Representation. CHE D5.8 report. Available at:

Google Scholar

Scholze, M., Chen, H., Kaminski, T., and Vossbeck, M. (2020a). Inversion Strategy Based on Joint QND Assessments, CHE D3.6 Report. Available at:

Google Scholar

Scholze, M., Kaminski, T., Knorr, W., Voßbeck, M., Wu, M., Ferrazzoli, P., et al. (2019). Mean European Carbon Sink over 2010-2015 Estimated by Simultaneous Assimilation of Atmospheric CO 2 , Soil Moisture, and Vegetation Optical Depth. Geophys. Res. Lett. 46, 13796–13803. doi:10.1029/2019GL085725

CrossRef Full Text | Google Scholar

Strandgren, J. (2020). Synthetic Satellite Dataset. CHE D2.5 report. Available at:

Google Scholar

Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C. (2020b). Uncertainty Analysis of a European High-Resolution Emission Inventory of CO2 and CO to Support Inverse Modelling and Network Design. Atmos. Chem. Phys. 20, 1795–1816. doi:10.5194/acp-20-1795-2020

CrossRef Full Text | Google Scholar

Super, I., Denier van der Gon, H. A. C., van der Molen, M. K., Dellaert, S. N. C., and Peters, W. (2020a). Optimizing a Dynamic Fossil Fuel CO2 Emission Model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an Urban Area Using Atmospheric Observations of CO2, CO, NOx, and SO2. Geosci. Model. Dev. 13, 2695–2721. doi:10.5194/gmd-13-2695-2020

CrossRef Full Text | Google Scholar

Thiemert, D. (2020). Final Dissemination and Exploitation Report. CHE D7.7 report. Available at:

Google Scholar

Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., et al. (2020). A Baseline for Global Weather and Climate Simulations at 1 Km Resolution. J. Adv. Model. Earth Syst. 12. doi:10.1029/2020MS002192

CrossRef Full Text | Google Scholar

Weir, B., Crisp, D., O'Dell, C., Basu, S., Chatterjee, A., Oda, T., et al. (2020). Regional Impacts of COVID-19 on Carbon Dioxide Detected Worldwide from Space. Available at:

Google Scholar

Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., et al. (2020). Satellite-based Estimates of Decline and Rebound in China's CO2 Emissions during COVID-19 Pandemic. Sci. Adv. 6 (49), eabd4998. doi:10.1126/sciadv.abd4998

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: carbon dioxide monitoring, green house gas emission, earth system approach, paris agreement, global stocktake

Citation: Balsamo G, Engelen R, Thiemert D, Agusti-Panareda A, Bousserez N, Broquet G, Brunner D, Buchwitz M, Chevallier F, Choulga M, Denier Van Der Gon H, Florentie L, Haussaire J-M, Janssens-Maenhout G, Jones MW, Kaminski T, Krol M, Le Quéré C, Marshall J, McNorton J, Prunet P, Reuter M, Peters W and Scholze M (2021) The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions. Front. Remote Sens. 2:707247. doi: 10.3389/frsen.2021.707247

Received: 09 May 2021; Accepted: 08 September 2021;
Published: 30 September 2021.

Edited by:

Jochen Landgraf, Netherlands Institute for Space Research (NWO), Netherlands

Reviewed by:

Zhao-Cheng Zeng, University of California, Los Angeles, United States
Rong Wang, Fudan University, China

Copyright © 2021 Balsamo, Engelen, Thiemert, Agusti-Panareda, Bousserez, Broquet, Brunner, Buchwitz, Chevallier, Choulga, Denier Van Der Gon, Florentie, Haussaire, Janssens-Maenhout, Jones, Kaminski, Krol, Le Quéré, Marshall, McNorton, Prunet, Reuter, Peters and Scholze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gianpaolo Balsamo,