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titleTable of Contents

Table of Contents

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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 Please note that the IFS cycle changes during the years, and this page documents the current operational cycle 48r1 (IFS Documentation CY48R1 - 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).


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)
  • 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).

Prescribed emissions and surface fluxes

  • Anthropogenic emissions from the CAMS-GLOB-ANT inventory with CAMS-TEMPO seasonal cycle (available from the ADS)
  • Injection and diurnal cycle of emissions 
  • CO2 fluxes over ocean from Jena CarboScope (v2020, Rödenbeck et al. 2013).
  • CH4 wetland fluxes from a climatology of the LPJ-HYMN dataset from Spahni et al. (2011).
  • 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 71) 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. 

titleTable 1: assimilated satellite observations



Space Agency

Data Provider








Full Physics v2.3.8




U. of Bremen (UB)


FOCAL v3.0





CH4, CO2

V10.1 (both) 

Evolution of the CAMS global greenhouse gases system

Implementation dateCycleSummary of changesResolution/Resolution changeNew species
27 February 202448r1
titleClick here to expand the description of the upgrade in CY48R1 for GHG forecasts

The CAMS IFS cycle 48R1 is based on ECMWF's IFS cycle 48R1. The new CY48R1 GHG o-suite also uses a flexible emission framework with prescribed sector-dependent diurnal cycle and emission height profile consistent with those used for the CAMS o-suite with chemistry and aerosol (Implementation of IFS cycle 48r1 for CAMS#Atmosphericcompositioncontentofthenewcycle). The anthropogenic emissions are from CAMS-GLOB-ANTv5.3 with monthly emission from CAMS-TEMPO. The biogenic CO2 fluxes are from the Farquhar model based on the implementation of Yin and Struik (2009) with C3/C4 photosynthesis pathways and a re-scaling of the soil moisture stress function, which was too limiting in the previous photosynthesis model used in CY74R3. The Gross Primary Production (GPP) from the Farquhar model is generally better than the previous A-gs photosynthesis model.  However, in CY48R1 there is an overestimation of GPP in the mid to high latitudes associated with an overestimation of the LAI in the current NWP climate fields (climate.v020). This LAI bias will be reduced in CY49R1 with the implementation of a new LAI climatology as part of the NWP land surface model developments (climate.v021).  The Biogenic Flux Adjustment Scheme (BFAS, Agusti-Panareda et al., 2016) has also been modified to only correct the ecosystem respiration fluxes. Further information on the new photosynthesis model can be found in (section 8.7.2).

The Semi-Lagrangian COMADH scheme (Malardel and Ricard, 2014) is also used for the advection of CO2 and CH4, as in the CAMS o-suite with chemistry and aerosol. The impact of COMADH is to reduce the global mass conservation error which means the correction from the mass fixer is smaller. The mass fixer has been slightly modified to be consistent with the mass fixer used in NWP for humidity and hydrometeors. It is still based on the Bermejo and Conde scheme, but it follows the multiplicative approach rather than the additive one (Diamantakis and Agusti-Panareda, 2017). The impact of the changes in the mass fixer is very small. 

titleClick here to expand the impact of the new cycle

The impact has been assessed using ICOS stations that provide CO2 and CH4 data in near-real-time:

  • The CO2 analysis is not able to correct for the model biases (control and analysis are very similar)
  • At high and mid-latitudes the CO2 concentration in the control and analysis has a large negative bias coming from the initial conditions of the GHG analysis. This is associated with a large positive bias in the LAI climatology at high and mid-latitudes (from climate fields in NWP climate.v020) which leads an overestimation in GPP during the growing season.  The LAI climatology will be updated in climate.v021 to be implemented in CY49R1 and this will lead to a reduction in the GPP and atmospheric CO2 bias.
  • The random error of CO2 is generally better in CY48R1 than in CY47R3.
  • The two ICOS sites at lower latitudes and tropics (Lampedusa, La Reunion) show an improvement in CO2 (bias and standard deviation). 
  • The CH4 analysis is better than in CY47R3 at many ICOS sites (associated mostly to changes in initial conditions that remove the large negative bias in CY47R3).
  • For both CO2 and CH4, the difference between control and analysis is very small at mid-latitudes, but it is larger and often better in the tropics due to the larger number of observations (from IASI).
Horizontal: 9 km, Vertical: 137 levels

Data access

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

Data organisation in MARS 

 CAMS Global greenhouse gases forecasts

CAMS Global greenhouse gases analysis










  • fc: forecasts
  • an: analyses
  • fc: forecasts
  • sfc: surface or single level
  • pl: pressure levels 
  • ml: model levels 
  • sfc: surface or single level
  • pl: pressure levels
  • ml: model levels

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. The analyses are available every 6 hours at 00 UTC, 06 UTC, 12 UTC and 18 UTC.

Data format

Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated.

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



2: Single level parameters (last reviewed on )

NameUnitsShortnameParam IDfcanNote

CO2 column-mean molar fraction

ppmtcco2210064xxThis variable is also known as XCO2

CH4 column-mean molar fraction

ppbtcch4210065xxThis variable is also known as XCH4

Total column Carbon monoxide

kg m-2tcco210127x

Accumulated Carbon Dioxide Net Ecosystem Exchange (NEE)

kg m-2aco2nee228080x

NEE is computed as a sum of GPP and Reco (see rows below). This flux has been corrected with the Biogenic Adjustment Scheme. 

NEE = corrected GPP + corrected Reco

Flux of Carbon Dioxide Net Ecosystem Exchange (NEE)

kg m-2 s-1fco2nee228083x

NEE is computed as a sum of GPP and Reco (see rows below). This flux has been corrected with the Biogenic Adjustment Scheme. 

NEE = corrected GPP + corrected Reco

Accumulated Carbon Dioxide Gross Primary Production (GPP)

kg m-2aco2gpp228081x

Uncorrected flux. For corrected flux, apply the scaling factor gppbfas to GPP:

Corrected GPP = Uncorrected GPP * gppbfas

Flux of Carbon Dioxide Gross Primary Production (GPP)

kg m-2 s-1fco2gpp228084x

Uncorrected flux. For corrected flux, apply the scaling factor gppbfas to GPP:

Corrected GPP = Uncorrected GPP* gppbfas

Accumulated Carbon Dioxide Ecosystem Respiration (Reco)

kg m-2aco2rec228082x

Uncorrected flux. For corrected flux, apply the scaling factor recbfas to Reco:

Corrected Reco = Uncorrected Reco * recbfas

Flux of Carbon Dioxide Ecosystem Respiration (Reco)

kg m-2 s-1fco2rec228085x

Uncorrected flux. For corrected flux, apply the scaling factor recbfas to Reco:

Corrected Reco = Uncorrected Reco * recbfas

GPP coefficient from Biogenic Flux Adjustment System


Scaling factor for GPP in Biogenic Flux Adjustment Scheme (BFAS) scheme (Agusti-Panareda et al., 2016 for further details)

Rec coefficient from Biogenic Flux Adjustment System

Scaling factor for GPP in Biogenic Flux Adjustment Scheme (BFAS) scheme (see Agusti-Panareda et al., 2016 for further details)

2m dewpoint temperature


2m temperature


10m u-component of wind

m s-110u165XX

10m v-component of wind

m s-110v166XX

10m wind gust in the last 3 hours

m s-110fg3228028XX

Boundary layer height


Cloud base height


Convective available potential energy

J kg-1cape59X

Convective inhibition

J kg-1cin228001XX

Convective precipitation



m of water equivalente182X

Forecast albedo

(0 - 1)fal243X

Friction velocity

m s-1zust228003X

Height of convective cloud top


High cloud cover

(0 - 1)hcc188XX

Lake cover

(0 - 1)cl26XX

Large-scale precipitation


Leaf area index, high vegetation

m2 m-2lai_hv67XX

Leaf area index, low vegetation

m2 m-2lai_lv66XX

Low cloud cover

(0 - 1)lcc186XX

Medium cloud cover

(0 - 1)mcc187XX

Potential evaporation


Precipitation type

code table (4.201)ptype260015X

Sea surface temperature

Sea-ice cover

(0 - 1)ci31XX

Skin reservoir content

m of water equivalent

Skin temperature


Snow albedo

(0 - 1)asn32XX

Snow depth

m of water equivalentsd141XX

Sunshine duration


Surface latent heat flux

J m-2slhf147X

Surface net solar radiationJ m-2ssr176X

Surface net solar radiation, clear skyJ m-2ssrc210X

Surface net thermal radiationJ m-2str177X

Surface net thermal radiation, clear skyJ m-2strc211X

Surface sensible heat fluxJ m-2sshf146XX
Surface solar radiation downward clear-skyJ m-2ssrdc228129X

Surface solar radiation downwardsJ m-2ssrd169X

Surface thermal radiation downward clear-skyJ m-2strdc228130X

Surface thermal radiation downwardsJ m-2strd175X

Total column cloud ice water

kg m-2tciw79XX

Total column cloud liquid water

kg m-2tclw78XX

Total column rain water

kg m-2tcrw228089XX

Total column snow water

kg m-2tcsw228090XX

Total column supercooled liquid water

kg m-2tcslw228088X

Total column water

kg m-2tcw136XX

Total precipitation


Total sky direct solar radiation at surface

J m-2fdir228021X

Vertically integrated moisture divergence

kg m-2vimd213X





3: Multi

level parameters

level parameters (last reviewed


on  )

NameUnitsShortnameParam IDfcanNote

Carbon dioxide mass mixing ratio

kg kg-1co2210061xx


kg kg-1ch4210062xx

Carbon monoxide mass mixing ratio

kg kg-1co210123x

Fraction of cloud cover

(0 - 1)cc248XX


m2 s-2z129XX

Logarithm of surface pressure




Potential vorticity

K m2 kg-1 s-1pv60XX

Relative humidity


Specific cloud ice water content

kg kg-1ciwc247XX

Specific cloud liquid water content

kg kg-1clwc246XX

Specific humidity

Specific rain water content

kg kg-1crwc75XX

Specific snow water content

kg kg-1cswc76XX



U-component of wind

m s-1u131XX
V-component of windm s-1v132XX

Vertical velocity

Pa s-1w135XX

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.

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

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



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This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS).

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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