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Introduction

The global production system is used to produce the daily forecasts of greenhouse gases, i.e. carbon dioxide (CO2) and methane (CH4) across the globe. Satellite observations of atmospheric composition are merged with a detailed computer simulation of the atmosphere using a method called data assimilation. The resulting analyses, i.e. maps of atmospheric composition, are used as initial conditions for the daily forecasts of atmospheric composition of long-lived greenhouse gases (i.e. CO2 and CH4). Analyses and forecasts for greenhouse gases are produced once a day. The analysis has a resolution of approximately 25km and it is produced 4 days behind real-time due to latency of satellite retrievals. The high-resolution forecast is run separately a few hours behind real time, with initial conditions based on a 4-day forecast of the analysis experiment, and it is also run at higher resolution (~9km). The lower cost of the CO₂ and CH₄ makes it possible to produce the analyses and forecasts at a higher resolution than the CAMS forecast of reactive gases and aerosols (~40km).

The IFS model and data assimilation system configurations for greenhouse gases 

The model used in the CAMS Global greenhouse gas forecasts is the Integrated Forecasting System (IFS) that also produces ECMWF weather forecasts, but with additional modules of greenhouse gases that have been developed within CAMS and precursor projects GEMS and MACC.

 The IFS model documentation for various model cycles can be found on https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation. Please note that the IFS cycle changes during the years, and this page documents the current operational cycle

49r1 (IFS Documentation

CY49R1 - Part VIII: Atmospheric Composition). The main components of the IFS model and data assimilation system for CO2 and CH4 are listed below (see References and IFS documentation link provided above for further details).

Model

The following processes of atmospheric composition are considered in the IFS model:

• transport of greenhouse gases 
• uptake and release of CO2 by vegetation and release of CO2 from soil over the land modelled by the ECLand surface model (Boussetta et al., 2021, Agusti-Panareda et al., 2014) based on the Farquhar photosynthesis model implementation by Yin and Struik (2009) with a Biogenic Flux Adjustment Scheme (BFAS, Agusti-Panareda et al., 2016) using LAI, vegetation types and cover from IFS. 

• The high-resolution forecast also includes carbon monoxide (CO) as a tracer with a simplified linear CO scheme based on Claeyman et al. (2010). It is initialised each day from the operational CAMS atmospheric composition analysis, which includes reactive species and aerosols (Implementation of IFS cycle 48r1 for CAMS#DocumentVersionsDocumentversions).

• A simple CH4 wetland model using climatological monthly wetland fraction maps, a Q10 factor for temperature sensitivity and a global scaling factor to constrain the global budget.

Prescribed emissions and surface fluxes

• Anthropogenic emissions from the CAMS-GLOB-ANT v6.2 inventory with CAMS-TEMPO seasonal cycle (available from the ADS)

• . The anthropogenic emission data are processed as input to the IFS simulation. 
• Height of release and diurnal cycle of emissions are described in the IFS documentation  https://www.ecmwf.int/en/elibrary/81374-ifs-documentation-cy48r1-part-viii-atmospheric-composition, please see Table 3.2 (page 27). Diurnal cycle of the emissions is considered
• CO2 fluxes over ocean from

• Jena CarboScope (v2020, Rödenbeck et al. 2013

• ).
• Biomass burning emissions inferred from satellite observations of fire activity using IS4FIRES injection heights (GFAS v1.4)
• Other CH4 emissions from oceans (Lambert and Schmidt, 1993), soil sink (Ridgwell et al., 1999), termites (Sanderson, 1996) and wild animals (Houweling et al., 1999).
• Climatology of CH4 chemical loss rate in the atmosphere from Bergamaschi et al. (2009).

Data assimilation system

The IFS uses a four-dimensional variational data assimilation method (4D-VAR) for the assimilation of a wide range of meteorological observations as well as satellite retrievals of atmospheric composition. The GHG analysis configuration has been documented by Massart et al. (2014, 2016) and Agusti-Panareda et al. (2023).

Observations assimilated

Satellite observations are used by CAMS to constrain the global forecast model, ensuring the forecasts are as accurate as possible. The process of merging the numerical forecast model with the observations is called data assimilation. CAMS produces global services in two modes: real-time chemistry and aerosol and real-time long-lived greenhouse gases.

The CAMS GHG production system uses currently satellite (GOSAT/TANSO, METOP-C/IASI) retrievals in 4D-Var data assimilation system to constrain the initial atmospheric state. Two categories of observations listed below (Table 1) are provided in near real-time (2-4 days behind). Column-averaged concentration retrievals of CO2 (XCO2) and XCH4 (CH4) using TANSO/GOSAT measurements are provided by the University of Bremen (UB) and SRON, respectively. The "Laboratoire de Météorologie Dynamique" (LMD) provides the mid-tropospheric columns of CH4 (MT-CH4) and of CO2 (MT-CO2) using both IASI/METOP and AMSU measurements. 

Instrument

Satellite

Space Agency

Data Provider

Species

Version

TANSO

GOSAT

JAXA

SRON

CH₄

Full Physics v2.3.8

TANSO

GOSAT

JAXA

U. of Bremen (UB)

CO₂

FOCAL v3.0

IASI

METOP-A/B/C

EUMETSAT/CNES

LMD

CH₄, CO₂

V10.1 (both) 

To enhance the accuracy of the analysis, a new background error matrix (B) has been created for each species (CO2 and CH4) using an ensemble data assimilation (EDA) method. This new EDA B matrix is built using 10 ensemble members through the perturbations of model physics tendencies, sea surface temperature, and observations. It is worth noting that until the last 48R1 cycle, the B matrix (called NMC-based B-matrix) was generated using forecast differences at different lead times (Parrish and Derber, 1992). The first results using the EDA-based B-matrix and the old B-matrix (NMC-based B-matrix) were presented recently as a poster at the ICOS science conference (Koffi et al., 2024). 

Observations assimilated

Satellite observations are used by CAMS to constrain the global forecast model, ensuring the forecasts are as accurate as possible. The process of merging the numerical forecast model with the observations is called data assimilation. CAMS produces global services in two modes: real-time chemistry and aerosol and real-time long-lived greenhouse gases.

The CAMS GHG production system uses currently satellite (GOSAT/TANSO, METOP-C/IASI) retrievals in 4D-Var data assimilation system to constrain the initial atmospheric state. Two categories of observations listed below (Table 1) are provided in near real-time (2-4 days behind). Column-averaged concentration retrievals of CO2 (XCO2) and CH4 (XCH4) using TANSO/GOSAT measurements are provided by the University of Bremen (UB) and SRON, respectively. The "Laboratoire de Météorologie Dynamique" (LMD) provides the mid-tropospheric columns of CH4 (MT-CH4) and of CO2 (MT-CO2) using both IASI/METOP and AMSU measurements. 

Evolution of the CAMS global greenhouse gases system

Evolution of the CAMS global greenhouse gases system

Data access

The data is now available from the Atmosphere Data Store (ADS), either interactively through its download web form or programmatically using the CDS API service:

CAMS global greenhouse gases forecasts

As this analysis of greenhouse gases is not available close to real time, it is not provided in the ADS. Instead, the high-resolution forecast is run a few hours behind real time, with initial conditions based on a 4-day forecast of the analysis experiment.

Users with direct access to MARS can browse the data on the MARS catalogue.

Data organisation in MARS 

Data availability (HH:MM)

CAMS Global greenhouse forecasts:

00 UTC forecast data availability guaranteed by 10:00 UTC

It is possible that the data will be available earlier but without guarantee.

Variations in delivery times may occur due to the non-operational nature of the ADS service, as issues may arise which cause delays

Spatial grid

CAMS Global greenhouse gases forecast and analysis have resolution of approximately 9 km (around 0.10 degrees for regular lat/lon grid) and 25 km (approximately 0.25 degrees), respectively. The data are archived either as spectral coefficients with a triangular truncation of Tco1279 or on a reduced Gaussian grid with a resolution of O1280 for the GHG forecast and Tco399 (O400) for the GHG analysis.

Temporal frequency

The CAMS Global greenhouse gases 5-day forecasts run daily from 00 UTC and the data are available every 3 hours.

Data format

Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated. NetCDF format is available on ADS but it is experimental.

Level listings

Pressure levels: 1000/950/925/900/850/800/700/600/500/400/300/250/200/150/100/70/50/30/20/10/7/5/3/2/1

Model levels: 1/to/137, which are described at L137 model level definitions. 

Parameter listings

Table 2: Single level parameters (last reviewed on 21 Oct 2024 )

Table 3: Multi level parameters (last reviewed on 21 Oct 2024 )

Evaluation and Quality Assurance reports

The global forecasting system is continually being evaluated to ensure the output meets the expected requirements. Comprehensive Evaluation and Quality Assurance (EQA) reports are provided on a quarterly basis. Before each upgrade of the global forecasting system, the new system is tested and evaluated, and a so-called "e-suite EQA report" is produced. All reports are available here.

Quality monitoring graphics

CAMS uses a multitude of independent data sets to routinely monitor its global forecasts. It works with various data providers, acquiring the observations with appropriate timeliness and generating graphics that show the differences between the forecasts and the independent observations. See at https://atmosphere.copernicus.eu/charts/packages/cams_monitoring/

Every day, CAMS provides also charts of the five-day forecasts of greenhouse gases here.

Guidelines

• Convert mass mixing ratio (MMR) to mass concentration or to volume mixing ratio (VMR)
• For details on how convert from mixing ratio (kg per kg dry air) to concentration (kg/m3): CAMS Surface concentration of a given species and Tech Memo by Malardel et al. (2019)

Anchor
references references

References

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