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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). 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.

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)

Evolution of the CAMS global greenhouse gases system

Implementation date

Cycle

Summary of changes

Resolution/Resolution change

New species

12 November 2024

49R1

A new climate version has been introduced in CY49R1 (climate.v021) with new LAI climatology and land use cover maps which affect the modelled biogenic fluxes of CO2. The BFAS reference climatology used to bias correct the biogenic CO2 fluxes has also been updated to use the CAMS inversion v23r2 and the new vegetation maps from climate.v021.
A new CH4 wetland model using climatological monthly wetland fraction maps based on GIEMS, a Q10 factor for temperature sensitivity and a global scaling factor to constrain the global budget.
CAMS-GLOB-ANT v6.2 emissions
New B matrix based on EDA
New initial conditions introduced on 22/12/2023 in the e-suite. The o-suite has been initialized with the e-suite on 12/11/2024.

Impact of CY49R1:

Improvement of the CO2 seasonal cycle at high latitudes associated with the new LAI climatology
Overall, there are neutral or small improvements compared to 48R1 o-suite (evaluation report). However, the 49R1 e-suite analysis tends to underestimate the surface concentrations over Europe (ICOS measurements) and this worsens in summer
Initial conditions (IC) from CAMS global inversions tend in general to improve the forecast, but for CO2, it partly plays a role in the underestimation of the surface concentrations
B matrix better resolves the vertical profile, but for CO2 it contributes to the underestimation of surface concentrations

27 February 2023

48R1

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 https://www.ecmwf.int/en/elibrary/81370-ifs-documentation-cy48r1-part-iv-physical-processes (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.

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

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

	CAMS Global greenhouse gases forecasts	CAMS Global greenhouse gases analysis
Stream	oper	oper
expver	0001	0011
class	gg	gg
Type	fc: forecasts	an: analyses fc: forecasts
Levtype	sfc: surface or single level pl: pressure levels ml: model levels	sfc: surface or single level pl: pressure levels ml: model levels

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.

PLEASE NOTE: CAMS Global atmospheric composition forecasts data available from the ADS has been pre-interpolated to a regular 0.1°x 0.1° latitude/longitude grid. The keyword 'grid' is not supported in CDS API requests on the ADS.

Temporal frequency

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

Please note data are available only from March 2024.

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

Please note that meteorological parameters have an embargo period of 5 days to the present. If you need meteorological parameters in real time please have a look at the ECMWF open data: real-time forecasts from IFS and AIFS

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

Name	Units	Shortname	Param ID	fc	an	Note
CO2 column-mean molar fraction	ppm	tcco2	210064	x	x	This variable is also known as XCO2
CH4 column-mean molar fraction	ppb	tcch4	210065	x	x	This variable is also known as XCH4
Total column Carbon monoxide	kg m^-2	tcco	210127	x
Accumulated Carbon Dioxide Net Ecosystem Exchange (NEE)	kg m^-2	aco2nee	228080	x		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^-1	fco2nee	228083	x		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^-2	aco2gpp	228081	x		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^-1	fco2gpp	228084	x		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^-2	aco2rec	228082	x		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^-1	fco2rec	228085	x		Uncorrected flux. For corrected flux, apply the scaling factor recbfas to Reco: Corrected Reco = Uncorrected Reco * recbfas
GPP coefficient from Biogenic Flux Adjustment System	dimensionless	gppbfas	228078	x		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	dimensionless	recbfas	228079	x		Scaling factor for GPP in Biogenic Flux Adjustment Scheme (BFAS) scheme (see Agusti-Panareda et al., 2016 for further details)
2m dewpoint temperature	K	2d	168	X	X
2m temperature	K	2t	167	X	X
10m u-component of wind	m s^-1	10u	165	X	X
10m v-component of wind	m s^-1	10v	166	X	X
10m wind gust in the last 3 hours	m s^-1	10fg3	228028	X	X
Boundary layer height	m	blh	159	X
Cloud base height	m	cbh	228023	X
Convective available potential energy	J kg^-1	cape	59	X
Convective inhibition	J kg^-1	cin	228001	X	X
Convective precipitation	m	cp	143	X	X
Evaporation	m of water equivalent	e	182	X
Friction velocity	m s^-1	zust	228003	X
Height of convective cloud top	m	hcct	228046	X
High cloud cover	(0 - 1)	hcc	188	X	X
Lake cover	(0 - 1)	cl	26	X	X
Land-sea mask	(0 - 1)	lsm	172	X
Large-scale precipitation	m	lsp	142	X
Leaf area index, high vegetation	m² m^-2	lai_hv	67	X	X
Leaf area index, low vegetation	m² m^-2	lai_lv	66	X	X
Low cloud cover	(0 - 1)	lcc	186	X	X
Mean sea level pressure	Pa	msl	151
Medium cloud cover	(0 - 1)	mcc	187	X	X
Potential evaporation	m	pev	228251	X
Precipitation type	code table (4.201)	ptype	260015	X
Sea surface temperature
Sea-ice cover	(0 - 1)	ci	31	X	X
Skin reservoir content	m of water equivalent
Skin temperature	K	skt	235	X	X
Snow depth	m of water equivalent	sd	141	X	X
Surface geopotential	m²s^-2	~	162051	X
Surface latent heat flux	J m^-2	slhf	147	X
Surface pressure	Pa	sp	134	X
Surface net solar radiation	J m^-2	ssr	176	X
Surface net solar radiation, clear sky	J m^-2	ssrc	210	X
Surface sensible heat flux	J m-2	sshf	146	X	X
Total cloud cover	(0 - 1)	tcc	164
Total column cloud ice water	kg m^-2	tciw	79	X	X
Total column cloud liquid water	kg m^-2	tclw	78	X	X
Total column rain water	kg m^-2	tcrw	228089	X	X
Total column snow water	kg m^-2	tcsw	228090	X	X
Total column supercooled liquid water	kg m^-2	tcslw	228088	X
Total column water	kg m^-2	tcw	136	X	X
Total precipitation	m	tp	228	X
Vertically integrated moisture divergence	kg m^-2	vimd	213	X

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

Name	Units	Shortname	Param ID	fc	an	Note
Carbon dioxide mass mixing ratio	kg kg^-1	co2	210061	X	X
Methane	kg kg^-1	ch4	210062	X	X
Carbon monoxide mass mixing ratio	kg kg^-1	co	210123	X
Fraction of cloud cover	(0 - 1)	cc	248	X	X	Data available only on model levels
Geopotential	m² s^-2	z	129	X	X	Only available on model level 1

Logarithm of surface pressure	Numeric	lnsp	152	X	X	Only available on model level 1
Potential vorticity	K m² kg^-1s^-1	pv	60	X	X	Data available only on pressure levels
Relative humidity	%	r	157	X	X	Data available only on pressure levels
Specific cloud ice water content	kg kg^-1	ciwc	247	X	X	Data available only on model levels
Specific cloud liquid water content	kg kg^-1	clwc	246	X	X	Data available only on model levels
Specific humidity
Specific rain water content	kg kg-1	crwc	75	X	X	Data available only on model levels
Specific snow water content	kg kg-1	cswc	76	X	X	Data available only on model levels
Temperature	K	t	130	X	X
U-component of wind	m s^-1	u	131	X	X
V-component of wind	m s^-1	v	132	X	X
Vertical velocity	Pa s-1	w	135	X	X

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)

Dataset DOI and Citation

Please refer to the "References" section on the catalogue entry page of this dataset in the Atmosphere Data Store (ADS) as it provides the DOI number as well as details on dataset citation and attribution.

References

Agustí-Panareda, A., Barré, J., Massart, S., Inness, A., Aben, I., Ades, M., Baier, B. C., Balsamo, G., Borsdorff, T., Bousserez, N., Boussetta, S., Buchwitz, M., Cantarello, L., Crevoisier, C., Engelen, R., Eskes, H., Flemming, J., Garrigues, S., Hasekamp, O., Huijnen, V., Jones, L., Kipling, Z., Langerock, B., McNorton, J., Meilhac, N., Noël, S., Parrington, M., Peuch, V.-H., Ramonet, M., Razinger, M., Reuter, M., Ribas, R., Suttie, M., Sweeney, C., Tarniewicz, J., and Wu, L.: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, 2023.
Agustí-Panareda, A., McNorton, J., Balsamo, G. et al. Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement. Sci Data 9, 160 (2022). https://doi.org/10.1038/s41597-022-01228-2
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D., 2019: Modelling CO₂ weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019
Agusti-Panareda, A., M. Diamantakis, V. Bayona, F. Klappenbach, and A. Butz, 2017: Improving the inter-hemispheric gradient of total column atmospheric CO₂ and CH₄ in simulations with the ECMWF semi-Lagrangian atmospheric global model, Geosci. Model Dev., 10, 1-18, https://doi.org/10.5194/gmd-10-1-2017
Agustí-Panareda, A., Massart, S., Chevallier, F., Balsamo, G., Boussetta, S., Dutra, E., and Beljaars, A., 2016: A biogenic CO₂ flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO₂ analyses and forecasts, Atmos. Chem. Phys., 16, 10399–10418, https://doi.org/10.5194/acp-16-10399-2016
Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D., 2014: Forecasting global atmospheric CO₂, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014
Boussetta, S.; Balsamo, G.; Arduini, G.; Dutra, E.; McNorton, J.; Choulga, M.; Agustí-Panareda, A.; Beljaars, A.; Wedi, N.; Munõz-Sabater, J.; et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere2021, 12, 723. https://doi.org/10.3390/atmos12060723
Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH₄emissions anomalies combining satellite measurements and high-resolution forecasts, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-550, in review, 2020.
Bergamaschi, P. et al. Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals. J. Geophys. Res. 114, D22301 (2009).
Bozzo, A. and Benedetti, A. and Flemming, J. and Kipling, Z. and Rémy, S., 2020: An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 3, 1007–1034, https://doi.org/10.5194/gmd-13-1007-2020
Claeyman, M. et al. A linear CO chemistry parameterization in a chemistry-transport model: Evaluation and application to data assimilation. Atmospheric Chem. Phys. 10, 6097–6115 (2010).Diamantakis, M. and Agustí-Panareda, A., 2017: A positive definite tracer mass fixer for high resolution weather and atmospheric composition forecasts, ECMWF Technical Memoranda, No. 819, 2017.
Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M., Diamantakis, M., Engelen, R. J., Gaudel, A., Inness, A., Jones, L., Josse, B., Katragkou, E., Marecal, V., Peuch, V.-H., Richter, A., Schultz, M. G., Stein, O., and Tsikerdekis, A., 2015: Tropospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015
Flemming, J. and Benedetti, A. and Inness, A. and Engelen, R. J. and Jones, L. and Huijnen, V. and Remy, S. and Parrington, M. and Suttie, M. and Bozzo, A. and Peuch, V.-H. and Akritidis, D. and Katragkou, E., 2017: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015, Atmos. Chem. Phys.,17, 1945-1983, https://doi.org/10.5194/acp-17-1945-2017
Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J. & Heimann, M. Inverse modeling of methane sources and sinks using the adjoint of a global transport model. J. Geophys. Res. Atmospheres 104, 26137–26160 (1999).
Huijnen, V. and Flemming, J. and Chabrillat, S. and Errera, Q. and Christophe, Y. and Blechschmidt, A.-M. and Richter, A. and Eskes, H., 2016: C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 9, 3071–3091, https://doi.org/10.5194/gmd-9-3071-2016
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M., 2019: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019
Inness, A. and Flemming, J. and Heue, K.-P. and Lerot, C. and Loyola, D. and Ribas, R. and Valks, P. and van Roozendael, M. and Xu, J. and Zimmer, W., 2019: Monitoring and assimilation tests with TROPOMI data in the CAMS system:
near-real-time total column ozone, Atmos. Chem. Phys., 19, 3939–3962, https://acp.copernicus.org/articles/19/3939/2019/
Inness, A. and Coauthors, 2015: Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS, Atmos. Chem. Phys., 15, 5275-5303, https://doi.org/10.5194/acp-15-5275-2015
Lambert, G. R. & Schmidt, S. Reevaluation of the oceanic flux of methane: uncertainties and long term variations. Chemosphere 26, 579–589 (1993).
Massart, S., Agusti-Panareda, A., Aben, I., Butz, A., Chevallier, F., Crevoisier, C., Engelen, R., Frankenberg, C., and Hasekamp, O., 2014: Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI, Atmos. Chem. Phys., 14, 6139–6158, https://doi.org/10.5194/acp-14-6139-2014
Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., Hilker, M., Burrows, J. P., Deutscher, N. M., Feist, D. G., Hase, F., Sussmann, R., Desmet, F., Dubey, M. K., Griffith, D. W. T., Kivi, R., Petri, C., Schneider, M., and Velazco, V. A., 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, https://doi.org/10.5194/acp-16-1653-2016
Rémy, S. and Kipling, Z. and Flemming, J. and Boucher, O. and Nabat, P. and Michou, M. and Bozzo, A. and Ades, M. and Huijnen, V. and Benedetti, A. and Engelen, R. and Peuch, V.-H. and Morcrette, J.-J., 2019: Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1), Geosci. Model Dev., 12, 4627-4659, https://doi.org/10.5194/gmd-12-4627-2019
Ridgwell, A. J., Marshall, S. J. & Gregson, K. Consumption of atmospheric methane by soils: A process-based model. Glob. Biogeochem. Cycles 13, 59–70 (1999).
Rödenbeck, C. et al. Global surface-ocean pCO2 and sea-Air CO2 flux variability from an observation-driven ocean mixed-layer scheme. Ocean Sci. 9, 193–216 (2013).
Sanderson, M. S. Biomass of termites and their emissions of methane and carbon dioxide: A global database. Glob. Biogeochem. Cycles 10, 543–557 (1996).
Spahni, R. et al. Constraining global methane emissions and uptake by ecosystems. Biogeosciences 8, 1643–1665 (2011).
X. Yin, P.C. Struik, C3 and C4 photosynthesis models: An overview from the perspective of crop modelling/ NJAS - Wageningen Journal of Life Sciences 57 (2009) 27–38

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